Underground water pollution tracing method and system based on block chain

Abstract

The invention relates to the technical field of block chains, in particular to an underground water pollution tracing method and system based on a block chain, and the method comprises the following steps: obtaining parameters such as permeation rate, aquifer thickness, water flow direction and the like, writing the parameters into a chain structure, generating a hydrological dynamic label, verifying pollution direction consistency to generate a space-time label, and screening credible nodes to generate a credible identifier. And calculating a diffusion range to form a liability list, and verifying emission records to generate a multi-chain evidence chain. According to the method, by recording hydrological parameters and pollutant concentration gradient differences in real time, generating diffusion threshold values, checking space-time consistency of pollution diffusion paths and screening credible node data, it is ensured that pollution source positioning and diffusion paths are accurate, a responsibility subject is determined through sequential logic checking, and a multi-chain mutual authentication data chain is generated; the credibility and transparency of pollution traceability are improved, the risk of data tampering is reduced, the cross-mechanism data sharing and cooperation efficiency is enhanced, and the completeness of a traceability chain is ensured.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a blockchain-based groundwater pollution tracing method and system. Background Art

[0002] The blockchain technology field encompasses core technologies such as distributed ledgers, peer-to-peer network transmission, cryptographic algorithms, and smart contracts. Its core objective is to achieve data immutability and multi-party collaborative trust mechanisms through a decentralized architecture. This field systematically covers data storage structure design, node consensus algorithm optimization, on-chain and off-chain interaction protocols, and privacy protection solutions. It focuses on addressing data silos, high trust costs, and traceability difficulties in traditional centralized systems. It has been gradually applied to scenarios such as financial transactions, supply chain management, digital identity authentication, and IoT device collaboration.

[0003] Among them, the blockchain-based groundwater pollution source tracing method uses the blockchain's hash chain data storage structure to write key parameters such as the pollutant concentration, geographic location, and detection time of the groundwater sampling point into the block in chronological order, and mark the node where the pollution incident occurred with a timestamp. This method addresses technical issues such as the tampering of pollution source location data, barriers to cross-institutional data sharing, and broken historical tracing chains. It uses multi-node distributed storage for sampling data, relies on a consensus mechanism to verify data authenticity, and combines smart contracts to automatically trigger the pollution diffusion model and the rules for associating responsible entities, ultimately forming a complete and credible chain of evidence of pollution behavior on the chain.

[0004] A shortcoming of existing technologies is that information silos are prone to appearing during data sharing, resulting in high trust costs between different institutions. Data recording and analysis of pollution incidents often suffer from inconsistencies in time and geographic coordinates, and the identification and attribution of pollution sources often rely on manual or traditional technical means, which can easily lead to data tampering or errors. Traditional technologies rely on static pollutant concentrations and incomplete hydrological flow parameters, making it difficult to reflect the dynamic process of groundwater pollution spread in real time, resulting in inefficient tracing and location of pollution incidents. Furthermore, existing technologies cannot effectively address cross-institutional data sharing and multi-party collaboration, especially in cross-chain data authentication and multi-dimensional tracing of pollution incidents. This leads to the risk of data omissions or broken traceability chains during pollution tracing. When locating pollution sources, existing methods may not accurately account for the actual changes in pollutants during their flow, making it difficult to provide relevant departments with accurate and reliable data support, which in turn affects the accountability of responsible parties and governance decisions. Summary of the Invention

[0005] The purpose of this invention is to solve the shortcomings of the existing technology and propose a blockchain-based groundwater pollution tracing method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a blockchain-based groundwater pollution tracing method, comprising the following steps:

[0007] S1: Obtain the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, write them into the hash chain storage structure of the blockchain at preset time intervals, generate a dynamic diffusion threshold based on the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness, verify whether the actual geographic coordinate offset exceeds the threshold range, and generate a hydrological dynamic chain verification tag;

[0008] S2: calling the hydrological dynamic chain verification tag, sorting the grid codes in time series according to the pollution event timestamp and geographic grid code of the target block, verifying the consistency of the hydrological flow direction of the migration direction and the water flow direction angle parameter, and generating a pollution spatiotemporal chain tag;

[0009] S3: Based on the pollution spatiotemporal chain tag, the pollutant concentration data and geographic coordinates of the blockchain monitoring node are called, and the distance attenuation relationship between the node and the pollution source and the concentration fluctuation range are combined to screen the logically self-consistent node data and generate the node data credibility identification;

[0010] S4: Based on the node data credibility identifier, the release timestamp of the candidate pollution source node is called, and a timing logic check is performed with the first detection timestamp of the downstream grid to calculate the pollution diffusion impact range and generate a chain association list of responsible entities.

[0011] As a further solution of the present invention, the hydrological dynamic chain verification tag includes chain parameters such as infiltration rate, aquifer thickness, and flow angle parameters written into the hash chain storage structure, pollutant concentration gradient difference, dynamic diffusion threshold range, and geographic coordinate offset verification results. The pollution spatiotemporal chain tag includes the pollution event timestamp, geographic grid coding sorting result, and migration direction and hydrological flow consistency verification identifier. The node data credibility identifier includes the blockchain monitoring node geographic coordinates, distance attenuation relationship coefficient, and concentration fluctuation range screening results. The responsible entity chain association list includes the candidate pollution source node release timestamp, downstream grid first detection timestamp timing verification result, and pollution diffusion impact range boundary parameters.

[0012] As a further solution of the present invention, the specific steps of S1 are:

[0013] S101: Collect the real-time infiltration rate, aquifer thickness, and flow angle of the groundwater sampling point, write the parameters and geographic coordinates into the blockchain chain storage structure at preset time intervals, and associate the previous block hash value; generate a real-time hydrological parameter hash chain;

[0014] S102: extracting the pollutant concentration gradient difference between adjacent blocks in the real-time hydrological parameter hash chain, and combining it with the aquifer thickness parameter of the current block to calculate the dynamic diffusion threshold reference value; generating a dynamic diffusion threshold coefficient;

[0015] S103: Based on the dynamic diffusion threshold coefficient, the geographic coordinates of the current block and the previous block in the real-time hydrological parameter hash chain are called, and the offset between the two is compared with the threshold interval relationship of the dynamic diffusion threshold coefficient. If the offset is outside the threshold interval, the verification is determined to be abnormal, and a hydrological dynamic chain verification tag is generated.

[0016] As a further solution of the present invention, the specific calculation formula for comparing the relationship between the offset and the threshold interval of the dynamic diffusion threshold coefficient is:

[0017]

[0018] Among them, D represents the Euclidean distance of the geographic coordinate offset between the current block and the previous block, k1 represents the lower threshold parameter of the dynamic diffusion threshold coefficient, k2 represents the upper threshold parameter, and H represents the hydrological dynamic chain verification parameter calculated based on the dynamic diffusion threshold coefficient and the offset.

[0019] As a further solution of the present invention, the specific steps of S2 are:

[0020] S201: Based on the hydrological dynamic chain verification tag, call the timestamp and geographic grid code of the pollution event in the target block, arrange the grid codes in ascending order according to the timestamp, establish a continuous distribution relationship of the grid codes based on the time of the event, and generate a spatiotemporal grid code sequence;

[0021] S202: Extracting the migration direction parameter and the water flow direction parameter of each grid code in the spatiotemporal grid code sequence, calculating the angle difference between the two, and comparing them with the water flow direction deviation threshold preset by the hydrological dynamic chain verification tag, selecting the grid codes whose difference values meet the threshold, and generating a direction consistency verification coefficient;

[0022] S203: Call the direction consistency check coefficient, extract the grid code with the check coefficient that meets the standard, connect them in series according to the time order of the spatiotemporal grid code sequence, superimpose the migration direction parameters to form a chain path, integrate the timestamp, grid code and migration direction parameters to construct a three-dimensional chain structure, and generate a contaminated spatiotemporal chain label.

[0023] As a further solution of the present invention, the specific steps of S3 are:

[0024] S301: Extract the pollution source code and timestamp based on the pollution spatiotemporal chain tag, call the pollutant concentration data and geographic coordinates of the blockchain monitoring node, associate the pollution source code and timestamp with the node concentration and location, and generate a node pollution parameter set;

[0025] S302: Extracting pollution source codes, node geographic coordinates, and pollutant concentrations from the node pollution parameter set, matching pollutant types based on the pollution source codes, and calculating node concentration correction values based on the distance attenuation coefficient corresponding to the type. Based on the fluctuation data, a reasonable concentration range is defined to generate an attenuation-corrected concentration interval.

[0026] S303: Compare the attenuation-corrected concentration interval with the actual concentration in the node pollution parameter set, screen node data that are logically self-consistent, assign a unique identifier based on the screening result, and generate a node data credibility identifier.

[0027] As a further solution of the present invention, the specific steps of S4 are:

[0028] S401: calling the node data credibility identifier, filtering nodes that meet the conditions based on the identifier, extracting the release timestamp, synchronously obtaining the first detection timestamp of the downstream grid, verifying the temporal logical relationship between the release timestamp and the detection timestamp, and generating a contaminated node set;

[0029] S402: Based on the set of polluted nodes and the spatiotemporal parameters of the pollution diffusion path, the diffusion relationship between the node and the downstream grid is verified, and the coverage grid range that meets the diffusion logic is counted to generate the diffusion coverage range;

[0030] S403: Integrate the polluted node set and the diffusion coverage, extract the node responsible entity identifier, build a chain association relationship between the responsible entity and the downstream grid according to the diffusion path level and release sequence, and output the responsible entity chain association list.

[0031] As a further solution of the present invention, the specific calculation formula based on the diffusion path level and release timing is:

[0032]

[0033] Among them, α k represents the weight attenuation coefficient of the path level k, Δt represents the absolute value of the release timing difference between the responsible entity and the downstream grid node, τ d represents the pollution diffusion rate parameter, ρ n represents the density of polluted nodes, Δη k represents the gradient value of pollution concentration changing with level k, and β represents the time series compensation factor.

[0034] As a further solution of the present invention, S5: based on the chain-linked list of responsible entities, calling the compliance emission record of the same node in the cross-chain interoperability interface, verifying the overlapping logic of the emission time and the pollution release time, and generating a multi-chain mutual authentication chain of evidence;

[0035] The multi-chain mutual authentication evidence chain includes cross-chain interoperability interface node data, compliance emission record overlapping logic, and inter-chain mutual recognition identification of emission timestamps and pollution release timestamps;

[0036] The specific steps of S5 are:

[0037] S501: Based on the responsible party identifier and cross-chain association path in the responsible party chain association list, call the cross-chain interoperability interface to extract the emission time interval, single emission amount, and pollution type code of the emission record under the same node, the responsible party level and cross-chain node identifier in the association list, and integrate them to generate an emission record parameter set;

[0038] S502: Based on the emission time interval in the emission record parameter set, extract the start time and end time of the pollution release time interval, compare the overlapping portion of the emission time interval with the pollution release time interval, calculate the ratio of the overlapping duration of the two to the total emission duration, filter out records with the ratio exceeding the compliance threshold, and generate a time overlap compliance record set;

[0039] S503: Based on the time-overlapping compliance record set, associate the cross-chain identifier and emission record hash value of the same responsible entity in the multi-chain nodes, extract the timestamp consistency, node identifier matching degree, and hash value correlation in the mutual recognition parameters, calculate the cross-chain mutual recognition coefficient, and generate a multi-chain mutual recognition evidence chain.

[0040] The blockchain-based groundwater pollution traceability system includes:

[0041] The dynamic threshold verification module collects the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, periodically writes them into the chain hash storage of the blockchain, combines the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness to calculate the dynamic diffusion threshold, compares the difference between the sampling point's geographic coordinate offset and the dynamic diffusion threshold, and generates a hydrological dynamic chain verification tag;

[0042] The spatiotemporal flow direction verification module calls the hydrological dynamic chain verification tag, extracts the pollution event timestamp and grid code sequence, sorts the grid codes by timestamp, calculates the cosine fit of the angle between the pollutant migration direction and the water flow direction, and generates a pollution spatiotemporal chain tag;

[0043] The node logic screening module calls the pollution spatiotemporal chain tag to obtain the pollutant concentration and geographic coordinate data of the monitoring node. Based on the Manhattan distance attenuation coefficient and concentration dispersion between the node and the pollution source, it screens the node data that meets the linear relationship between distance and concentration, and generates a node data credibility identifier;

[0044] The responsibility time series association module calls the node data credibility identifier, extracts the release timestamp of the pollution source node and the first detection timestamp of the downstream grid, calculates the product of the timestamp difference and the diffusion rate, generates the diffusion impact sector area based on the grid coverage area, and generates a chain association list of responsible entities;

[0045] The cross-chain evidence mutual verification module calls the chain association list of the responsible entity, traverses the compliance emission records of the target node in the cross-chain interface, verifies the intersection length of the emission record timestamp set and the pollution release timestamp, and generates a multi-chain mutual verification evidence chain.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by real-time recording of hydrological parameters and pollutant concentration gradient differences, diffusion thresholds are generated, the spatiotemporal consistency of pollution diffusion paths is verified, and trusted node data is screened to ensure the accuracy of pollution source positioning and diffusion paths. The responsible party is determined by using temporal logic verification and a multi-chain mutual authentication chain is generated, which improves the credibility and transparency of pollution traceability, reduces the risk of data tampering, and enhances the efficiency of cross-institutional data sharing and collaboration, ensuring the integrity of the traceability chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the steps of the present invention

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0052] See also Figure 1 ,The groundwater pollution tracing method based on blockchain includes the following steps:

[0053] S1: Obtain the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, write them into the hash chain storage structure of the blockchain at preset time intervals, generate a dynamic diffusion threshold based on the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness, verify whether the actual geographic coordinate offset exceeds the threshold range, and generate a hydrological dynamic chain verification tag;

[0054] S2: Call the hydrological dynamic chain verification tag, sort the grid codes by time series according to the pollution event timestamp and geographic grid code of the target block, verify the hydrological flow consistency of the migration direction and the water flow direction angle parameter, and generate the pollution spatiotemporal chain tag;

[0055] S3: Based on the pollution spatiotemporal chain tag, the pollutant concentration data and geographic coordinates of the blockchain monitoring nodes are called. Combined with the distance attenuation relationship between the node and the pollution source and the concentration fluctuation range, logically self-consistent node data is screened to generate a node data credibility identifier;

[0056] S4: Based on the node data credibility identifier, the release timestamp of the candidate pollution source node is called, and a temporal logic check is performed with the first detection timestamp of the downstream grid to calculate the pollution diffusion impact range and generate a chain association list of responsible entities;

[0057] S5: Based on the chain-linked list of responsible entities, call the compliance emission records of the same node in the cross-chain interoperability interface, verify the overlapping logic of the emission time and the pollution release time, and generate a multi-chain mutual authentication certificate chain.

[0058] The hydrological dynamic chain verification tags include the chain parameters of the infiltration rate, aquifer thickness, and flow angle parameters written into the hash chain storage structure, the pollutant concentration gradient difference, the dynamic diffusion threshold range, and the geographic coordinate offset verification results. The pollution spatiotemporal chain tags include the pollution event timestamp, the geographic grid coding sorting results, and the migration direction and hydrological flow direction consistency verification mark. The node data credibility mark includes the blockchain monitoring node geographic coordinates, the distance attenuation relationship coefficient, and the concentration fluctuation range screening results. The responsible entity chain association list includes the candidate pollution source node release timestamp, the downstream grid first detection timestamp timing verification result, and the pollution diffusion impact range boundary parameters. The multi-chain mutual recognition evidence chain includes cross-chain interoperability interface node data, compliance emission record overlap logic, and inter-chain mutual recognition marks of emission timestamps and pollution release timestamps.

[0059] See also Figure 1 , the specific steps of S1 are:

[0060] S101: Collect the real-time infiltration rate, aquifer thickness, and flow angle of the groundwater sampling point, write the parameters and geographic coordinates into the blockchain chain storage structure at preset time intervals, and associate the previous block hash value; generate a real-time hydrological parameter hash chain;

[0061] The implementation process first involves collecting real-time data on groundwater sampling points, including infiltration rate, aquifer thickness, and flow angle. Specialized monitoring equipment is installed to obtain relevant hydrological parameters in real time. Infiltration rate is determined using a permeameter, aquifer thickness is measured using a depth-diagnosing instrument, and flow angle is obtained using an electronic level or global positioning system (GNSS). In practice, data is collected regularly, for example, every 30 minutes, recording the current groundwater infiltration rate, aquifer depth, and flow angle. For example, assume a sampling point has an infiltration rate of 0.5 cm / s, an aquifer thickness of 12 meters, and a flow angle of 45°. This data, along with its geographic coordinates, is then uploaded to the blockchain. During data upload, a hashing algorithm is used to ensure the uniqueness and security of each data set, linking it to the hash value of the previous block. This chain structure ensures data immutability and efficient traceability. The resulting hydrological data chain allows real-time query of historical data and trends for each sampling point, ensuring data integrity and reliability, facilitating subsequent analysis and decision-making.

[0062] S102: Extract the pollutant concentration gradient difference between adjacent blocks in the real-time hydrological parameter hash chain, combine it with the aquifer thickness parameter of the current block, calculate the dynamic diffusion threshold reference value; and generate the dynamic diffusion threshold coefficient;

[0063] During execution, the pollutant concentration differences between adjacent blocks are extracted from the real-time hydrological parameter hash chain and combined with the aquifer thickness of the current block to calculate the dynamic diffusion threshold baseline value. Assume that the pollutant concentrations in two adjacent blocks are 50 mg / L and 70 mg / L, respectively, and the aquifer thickness of the current block is 10 meters. First, the difference in pollutant concentrations is calculated, resulting in a concentration difference of 20 mg / L. This concentration difference is then combined with the aquifer thickness to calculate the dynamic diffusion threshold baseline value using a pre-set empirical formula. For example, assuming a constant of 5, the result is the concentration difference of 20 multiplied by the aquifer thickness of 10 divided by the constant of 5, resulting in a baseline value of 40 mg / L·m. This baseline value provides the basis for the subsequent calculation of the dynamic diffusion threshold coefficient, which can be used to predict the range and extent of pollutant diffusion. The calculation and setting of this value depends on the specific hydrological environment and can be flexibly adjusted to suit the geological conditions and pollution source characteristics of different regions, thereby improving the accuracy and adaptability of the model.

[0064] S103: Based on the dynamic diffusion threshold coefficient, the geographic coordinates of the current block and the previous block in the real-time hydrological parameter hash chain are called, and the offset between the two is compared with the threshold interval relationship of the dynamic diffusion threshold coefficient. If the offset is outside the threshold interval, the verification is determined to be abnormal, and a hydrological dynamic chain verification tag is generated;

[0065] The specific calculation formula for comparing the relationship between the offset and the threshold interval of the dynamic diffusion threshold coefficient is:

[0066]

[0067] Where D represents the Euclidean distance between the geographic coordinate offset of the current block and the previous block, k1 represents the lower threshold parameter of the dynamic diffusion threshold coefficient, k2 represents the upper threshold parameter, and H represents the hydrological dynamic chain verification parameter calculated based on the dynamic diffusion threshold coefficient and the offset;

[0068] D is the Euclidean distance between the current block and the previous block’s geographic coordinate offset, which is obtained and calculated in real time by the geographic coordinate monitoring equipment. The calculation formula is:

[0069] For example: the previous block coordinates are (102.34°E, 30.25°N), and the current block coordinates are (102.36°E, 30.27°N).

[0070]

[0071] k1 and k2 are the lower and upper threshold parameters of the dynamic diffusion threshold coefficient, which are set based on the hydrological dynamic chain calibration standard and obtained by fitting historical hydrological data statistics and real-time monitoring data.

[0072] For example, when the diffusion rate of the watershed is within the safe range, k1 = 0.15, k2 = 0.45; when the diffusion rate exceeds the safe threshold, k1 = 0.30, k2 = 0.60. Here, k1 = 0.15, k2 = 0.45 is taken;

[0073] Substitute into the formula to calculate the numerator:

[0074] The denominator is calculated as

[0075] Finally

[0076] The result shows that H is a negative value, which means that the current geographic coordinate offset is lower than the lower threshold parameter of the dynamic diffusion threshold coefficient. The calibration is normal and there is no need to generate a hydrological dynamic chain calibration label. When H ≥ 0, the offset exceeds the threshold interval, triggering a calibration anomaly and generating a label. The value of D is collected in real time by geographic coordinate monitoring equipment, and the values of k1 and k2 are set based on the regression analysis of historical data of hydrological dynamic diffusion rate and are dynamically adjusted according to the real-time monitored watershed hydrological status.

[0077] See also Figure 1 , the specific steps of S2 are:

[0078] S201: Based on the hydrological dynamic chain verification tag, call the timestamp and geographic grid code of the pollution event in the target block, arrange the grid codes in ascending order by timestamp, establish a continuous distribution relationship of the grid codes based on the time of event occurrence, and generate a spatiotemporal grid code sequence;

[0079] Based on the hydrological dynamic chain verification tag, the first step is to obtain the timestamps and geographic grid codes of the pollution events in the target block, and then sort the grid codes in ascending order by timestamp. Specifically, during the processing, the timestamps and corresponding geographic grid codes are extracted from the pollution events, and then they are sorted according to the order of the timestamps. This operation can be implemented using common data processing tools, such as the sort function in the Pandas library. This operation can arrange the grid codes in chronological order, establishing a continuous distribution relationship of grid codes based on the time of event occurrence. For example, if a pollution event occurs on April 1, April 2, and April 3, 2025, corresponding to grid codes A, B, and C, respectively, the resulting spatiotemporal grid code sequence after sorting will be A (April 1), C (April 2), and B (April 3). This process helps to clearly demonstrate the distribution of pollution events in time and space, facilitating subsequent pollution tracking and analysis.

[0080] S202: Extract the migration direction parameter and water flow direction parameter of each grid code in the spatiotemporal grid code sequence, calculate the angle difference between the two, and compare them with the water flow direction deviation threshold preset by the hydrological dynamic chain verification tag. Filter the grid codes whose difference values meet the threshold and generate a direction consistency verification coefficient.

[0081] To extract the migration direction and flow direction parameters for each grid code in a spatiotemporal grid code sequence, these directional data must first be obtained. Migration direction refers to the direction of pollutant flow from the pollution source to the target grid, while flow direction is determined by the flow pattern or hydrological conditions of the watershed. After obtaining these two directional parameters, the next step is to calculate the angular difference between them. This angular difference is calculated by comparing the directional angles. For example, if the migration direction of a grid is 45° and the flow direction is 60°, the angular difference is 15°. This angular difference is then compared with a preset flow direction deviation threshold. If the difference is less than or equal to the threshold, the migration direction is considered consistent with the flow direction; otherwise, it is considered inconsistent. For example, if the flow direction deviation threshold is set to 20°, a 15° angular difference is considered consistent, and the data is screened and a directional consistency verification coefficient is generated. This process helps determine whether the spread of pollutants is consistent with the flow direction, thereby assessing the rationality of pollution spread.

[0082] S203: Calling the direction consistency check coefficient, extracting the grid codes with the check coefficients that meet the standards, concatenating them in chronological order according to the spatiotemporal grid code sequence, superimposing the migration direction parameters to form a chain path, integrating the timestamp, grid code, and migration direction parameters to construct a three-dimensional chain structure, and generating a contaminated spatiotemporal chain label;

[0083] After filtering out qualified grid codes through the direction consistency check coefficient, they are then concatenated in chronological order according to the spatiotemporal grid code sequence, and the migration direction parameters are superimposed to form a chain path. Specifically, each grid code is connected into a path according to the chronological order in the spatiotemporal grid code sequence. This path reflects the transmission process of pollutants from one grid to another. In this process, the timestamp, grid code, and migration direction parameters need to be integrated into a data structure to construct a three-dimensional chain structure. For example, assuming that the chain path of a pollution event is A→B→C, the timestamps are April 1, April 2, and April 3, and the migration direction parameters are southeast, northeast, and east, the final three-dimensional chain structure will contain timestamps, grid codes, and their corresponding migration directions. On this basis, the spatiotemporal distribution of pollutants can be clearly displayed, providing strong support for subsequent pollution tracking and prevention.

[0084] See also Figure 1 , the specific steps of S3 are:

[0085] S301: Extract the pollution source code and timestamp based on the pollution spatiotemporal chain tag, call the pollutant concentration data and geographic coordinates of the blockchain monitoring node, associate the pollution source code and timestamp with the node concentration and location, and generate a node pollution parameter set;

[0086] First, it is necessary to extract two key data: the pollution source code and the timestamp. The pollution source code is a unique identifier for each pollution source, while the timestamp is used to record the time of data collection, thereby ensuring the timeliness of the data. In this process, the pollutant concentration and geographic coordinates are obtained through the blockchain monitoring node. The role of the blockchain is to ensure that the data of each monitoring node cannot be tampered with and can be traced efficiently. In the specific implementation, the pollutant concentration (for example, PM2.5 concentration or nitrogen dioxide concentration) and geographic location (such as longitude and latitude coordinates) of each monitoring node will be stored through the blockchain and associated with the pollution source code and timestamp to form a set of pollution parameters. For example, a node with a pollution source code of "PS01" has a pollutant concentration of 40 micrograms per cubic meter (μg / m 3 ), the coordinates are longitude -74.0060, latitude 40.7128, and the timestamp is "2025-04-2912:00:00". This set of data will be stored in the blockchain and bound to the pollution source code and timestamp to form a complete set of node pollution parameters.

[0087] S302: Extract the pollution source code, node geographic coordinates, and pollutant concentration from the node pollution parameter set, match the pollutant type based on the pollution source code, and calculate the node concentration correction value based on the distance attenuation coefficient corresponding to the type. Define the reasonable concentration range based on the fluctuation data and generate the attenuation correction concentration interval;

[0088] First, the accuracy of each data item must be verified. The pollution source code identifies the type of pollution source, the geographic coordinates reflect the location of the monitoring equipment, and the pollutant concentration is the specific value of the pollutant detected at that node. Next, the pollution source code must be matched to the corresponding pollutant type. For example, a pollution source coded "PS01" may correspond to pollutants such as PM2.5, sulfur dioxide, or carbon monoxide. Depending on the pollutant type, a corresponding distance attenuation factor can be used to correct the concentration. The concentration of each pollutant gradually decreases with distance, so the attenuation factor can be used to adjust the pollutant concentration based on distance. Specifically, if the concentration at a monitoring node is 40 micrograms per cubic meter and the node is 2 kilometers away from the pollution source, the distance attenuation factor can be applied to correct the concentration. During this process, a reasonable concentration fluctuation range can be defined, typically within a certain percentage, such as 5%. Ultimately, the corrected concentration value will fluctuate within a reasonable range, representing the true value of the pollutant concentration at that node after adjusting for distance attenuation.

[0089] S303: Compare the attenuation-corrected concentration interval with the actual concentration in the node pollution parameter set, screen node data that are logically self-consistent, assign a unique identifier based on the screening result, and generate a node data credibility identifier;

[0090] First, it's necessary to determine whether the actual concentration at each node is within a reasonable range. For example, if the pollutant concentration at a node is within the set fluctuation range, the data at that node is considered reasonable. If the concentration at a node exceeds the reasonable range, it may indicate a data anomaly and require further screening and verification. During the screening process, the data at each node can be evaluated using a preset concentration threshold. If the data meets the predetermined threshold, the node data is considered reliable. Conversely, if the data exceeds the threshold, it should be marked as unreliable. To ensure data accuracy, the credibility mark generation process should include verification of multiple factors, such as whether the sensor is functioning properly, whether the data is subject to external interference, and whether the data collection complies with specified sampling standards. For example, if the pollutant concentration at a node is 30 micrograms per cubic meter, while the calibrated reasonable range is 24.3 to 26.9 micrograms per cubic meter, the data does not meet the expected range and should be marked as unreliable. If the actual concentration is 25 micrograms per cubic meter and falls within the reasonable range, the data is considered reliable and can be used as a basis for further analysis.

[0091] See also Figure 1 , the specific steps of S4 are:

[0092] S401: Call the node data credibility identifier, filter the nodes that meet the conditions based on the identifier, extract the release timestamp, synchronously obtain the first detection timestamp of the downstream grid, verify the temporal logical relationship between the release timestamp and the detection timestamp, and generate a polluted node set;

[0093] First, the node data credibility flag is retrieved. Qualified nodes are screened based on the flag, and the release timestamp is extracted. Simultaneously, the first detection timestamp of the downstream grid is obtained. Next, the timing logic of the release and detection timestamps is verified to generate a set of contaminated nodes. Node data credibility flags are typically derived from monitoring equipment or historical records and serve to indicate data reliability. The release timestamp refers to the specific time when the pollutant is released from the pollution source, typically recorded in real time by sensors or monitoring. The first detection timestamp of the downstream grid refers to the time when the pollutant is first detected as it spreads to the downstream area, typically obtained by monitoring equipment in the downstream grid. The release and detection timestamps are verified to ensure that the detection timestamp is later than the release timestamp. This temporal relationship reflects the actual path of pollutant diffusion. If the time difference conforms to reasonable diffusion logic, the pollutant propagated to the downstream grid within the specified timeframe, meeting the timing requirements. For example, if the pollution source release time is 12:00 and the downstream grid first detects it at 12:15, this indicates that the pollutant took 15 minutes to diffuse to that grid, conforming to reasonable diffusion timing logic. Through this verification process, pollution nodes that conform to the diffusion time sequence logic can be screened out and further used for pollution control and governance.

[0094] S402: Based on the set of polluted nodes and the spatiotemporal parameters of the pollution diffusion path, the diffusion relationship between the node and the downstream grid is verified, and the coverage grid range that meets the diffusion logic is counted to generate the diffusion coverage range;

[0095] The spatiotemporal parameters of a pollution diffusion path include the speed and direction of pollutant diffusion, as well as environmental factors such as wind speed, temperature, and humidity. These factors directly influence the speed and range of pollutant diffusion. By developing mathematical and physical models and integrating them with actual environmental parameters, we can predict the diffusion trajectory of pollutants. For example, if a pollution source releases pollutants at a diffusion rate of 1 meter per minute, simulations predict that the pollutant will reach downstream grid cells in 20 minutes. This prediction can be adjusted based on changing environmental conditions. By comparing detection data from downstream grid cells with the diffusion model, the pollutant's diffusion range can be determined and the grid areas that meet the diffusion logic can be counted. In practice, if environmental factors predict that pollutants will cover a certain grid area within 1 hour after release, this predicted range can be used to determine the actual diffusion area, thereby obtaining a more accurate diffusion range. This process can help decision makers effectively assess the environmental impact of pollutants and provide a basis for subsequent control and prevention efforts.

[0096] S403: Integrate the set of polluted nodes and the diffusion coverage, extract the node responsible party identifier, build a chain association relationship between the responsible party and the downstream grid according to the diffusion path level and release time sequence, and output the chain association list of the responsible party;

[0097] The specific calculation formula based on the diffusion path level and release timing is:

[0098]

[0099] Among them, α k represents the weight attenuation coefficient of the path level k (k∈[1,n] level sequence), Δt represents the absolute value of the release timing difference between the responsible entity and the downstream grid node, τ d represents the pollution diffusion rate parameter (unit: m2 / s), ρ n Represents the pollution node density (number of nodes / km 2 ), Δη k represents the gradient value of pollution concentration changing with level k, β represents the time series compensation factor (β∈(0,1]);

[0100] Pollution diffusion rate parameter τ d Based on environmental monitoring data, the measured diffusion rate in an industrial area is 1.2m 2 / s, which is consistent with the typical urban atmospheric diffusion rate range of 0.5-2.0m 2 / s;

[0101] Polluted node density ρ n Through grid monitoring statistics, it is 8 nodes / km 2 , which is consistent with the industrial emission node density range of 5-15 nodes / km in this area.2 ;

[0102] The release time difference Δt was calculated by comparing the pollution source emission records with the downstream grid monitoring timestamps to be 10800 seconds;

[0103] Path level attenuation coefficient α k According to the pollution diffusion path hierarchical weight model, when level k = 1, α1 = 0.8; when k = 2, α2 = 0.6; when k = 3, α3 = 0.4; the hierarchical weight attenuation coefficient decreases linearly with the increase of the level, and the decreasing amplitude is 0.2 / level;

[0104] Pollution concentration gradient Δη k Calculated by measuring the concentration difference of each level, when level k = 1, Δη1 = 0.5 mg / m 3 When k = 2, Δη2 = 0.3 mg / m 3 When k = 3, Δη3 = 0.2 mg / m 3 The concentration gradient decreases by 0.1-0.3 mg / (m 3 · level);

[0105] The timing compensation factor β is set to 0.7 based on the analysis of the timing impact of historical diffusion events, which is consistent with the empirical range of the timing compensation factor of 0.5-0.9.

[0106] Calculation process:

[0107] When level k=1, substitute the parameters:

[0108]

[0109] When level k = 2:

[0110]

[0111] When level k=3:

[0112]

[0113] Pollution concentration gradient term:

[0114] (0.5 0.7) + (0.3 0.7) + (0.2 0.7) = 0.35 + 0.21 + 0.14 = 0.7;

[0115] sum:

[0116] W k =4063.5+3047.6+2031.7+0.7=9143.5;

[0117] The results show that the total weight of the path levels is 9143.5, the weight of level k=1 accounts for 44.4%, level k=2 accounts for 33.3%, and level k=3 accounts for 22.2%. The weight distribution reflects that the lower the level in the pollution diffusion path, the greater the impact; after the weight value is superimposed on the concentration gradient correction term, it is used to construct a chain association relationship between the responsible entity and the downstream grid. The higher the weight value, the higher the level association priority.

[0118] See also Figure 1 , the specific steps of S5 are:

[0119] S501: Based on the responsible party identifier and cross-chain association path in the responsible party chain association list, call the cross-chain interoperability interface to extract the emission time interval, single emission amount, and pollution type code of the emission record under the same node, and integrate the responsible party level and cross-chain node identifier in the association list to generate the emission record parameter set;

[0120] First, it is necessary to obtain the responsible entity identifier and cross-chain association path from the responsible entity chain association list. The responsible entity identifier is a unique identifier for each responsible entity, such as "Entity A" or "Entity B." The cross-chain association path refers to the responsible entity in different mapping paths, including the cross-chain identifier of each node. By calling the cross-chain interoperability interface, emission records are retrieved. The record content includes the emission time interval, single emission amount, and pollution type code. The emission time interval refers to the start and end time of the emission event, the single emission amount represents the total amount of pollutants in each emission activity, and the pollution type code is a specific code used to distinguish different pollution sources. For example, if an enterprise emits a specific amount of pollutants within a certain time period, its emission record will include the time interval, emission amount, and pollution type code. The responsible entity hierarchy and cross-chain node identifiers in the association list ensure that this data is correctly attributed to the corresponding responsible entity and further integrated into the emission record parameter set as the basis for subsequent analysis.

[0121] S502: Based on the emission time interval in the emission record parameter set, extract the start time and end time of the pollution release time interval, compare the overlapping portion of the emission time interval with the pollution release time interval, calculate the ratio of the overlapping duration to the total emission duration, filter out records with a ratio exceeding the compliance threshold, and generate a time overlap compliance record set;

[0122] The start and end times of the pollution release time interval are extracted from the emission time interval in the emission record parameter set. The emission time interval refers to the time period during which emissions actually occurred, while the pollution release time interval represents the time period during which pollutants were released. The overlap between the two is crucial for calculations. For example, if an enterprise emitted pollutants during a certain period, but the pollution release time interval was slightly different, by comparing the emission time interval with the pollution release time interval, the overlap between the two can be calculated. This method accurately determines the overlap between the two time periods. For example, if the total duration of the emission activity is three days and the overlap between the emission time interval and the pollution release time interval is two days, the ratio of the overlap to the total emission time interval is 2 / 3. This ratio is compared with the set compliance threshold. If the ratio exceeds the compliance threshold, the record is considered compliant, and a time overlap compliance record set is generated, providing a basis for subsequent actions.

[0123] S503: Based on the time-overlapping compliance record set, associate the cross-chain identifiers and emission record hash values of the same responsible entity in the multi-chain nodes, extract the timestamp consistency, node identifier matching degree, and hash value correlation in the mutual recognition parameters, calculate the cross-chain mutual recognition coefficient, and generate a multi-chain mutual recognition evidence chain;

[0124] Based on the set of time-overlapping compliance records, it is necessary to correlate the cross-chain identifiers and emission record hash values of the same responsible entity across multiple chain nodes. The cross-chain identifier uniquely identifies the responsible entity across chains, while the hash value is the encrypted result of the emission record, ensuring data integrity. During this step, mutual recognition parameters are extracted, including timestamp consistency, node identifier matching, and hash value correlation. Timestamp consistency assesses the time proximity of records across chains, node identifier matching verifies the consistency of the responsible entity's identifiers across chains, and hash value correlation compares the hash values of records across chains to ensure data has not been tampered with during transmission. These mutual recognition parameters can be further calculated to assess the degree of mutual recognition between multiple chains. These evaluation results ultimately generate a multi-chain mutual recognition evidence chain, providing a reliable basis for cross-chain data exchange.

[0125] See also Figure 2 , a groundwater pollution traceability system based on blockchain, including:

[0126] The dynamic threshold verification module collects the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, periodically writes them into the chain hash storage of the blockchain, combines the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness to calculate the dynamic diffusion threshold, compares the difference between the sampling point's geographic coordinate offset and the dynamic diffusion threshold, and generates a hydrological dynamic chain verification tag;

[0127] The spatiotemporal flow direction verification module calls the hydrological dynamic chain verification tag, extracts the pollution event timestamp and grid code sequence, sorts the grid codes by timestamp, calculates the cosine fit of the angle between the pollutant migration direction and the water flow direction, and generates the pollution spatiotemporal chain tag;

[0128] The node logic screening module calls the pollution spatiotemporal chain tag to obtain the pollutant concentration and geographic coordinate data of the monitoring node. Based on the Manhattan distance attenuation coefficient and concentration dispersion between the node and the pollution source, it screens the node data that meets the linear relationship between distance and concentration and generates the node data credibility mark.

[0129] The responsibility time series association module calls the node data credibility identifier, extracts the release timestamp of the pollution source node and the first detection timestamp of the downstream grid, calculates the product of the timestamp difference and the diffusion rate, and generates the diffusion impact sector area based on the grid coverage area, and generates a chain association list of responsible entities;

[0130] The cross-chain evidence mutual verification module calls the chain association list of responsible entities, traverses the compliance emission records of the target node in the cross-chain interface, verifies the intersection length of the emission record timestamp set and the pollution release timestamp, and generates a multi-chain mutual authentication evidence chain.

[0131] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The groundwater pollution tracing method based on blockchain is characterized by: The following steps are involved: S1: Obtain the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, write them into the hash chain storage structure of the blockchain at preset time intervals, generate a dynamic diffusion threshold based on the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness, verify whether the actual geographic coordinate offset exceeds the threshold range, and generate a hydrological dynamic chain verification tag; S2: calling the hydrological dynamic chain verification tag, sorting the grid codes in time series according to the pollution event timestamp and geographic grid code of the target block, verifying the consistency of the hydrological flow direction of the migration direction and the water flow direction angle parameter, and generating a pollution spatiotemporal chain tag; S3: Based on the pollution spatiotemporal chain tag, the pollutant concentration data and geographic coordinates of the blockchain monitoring node are called, and the distance attenuation relationship between the node and the pollution source and the concentration fluctuation range are combined to screen the logically self-consistent node data and generate the node data credibility identification; S4: Based on the node data credibility identifier, the release timestamp of the candidate pollution source node is called, and a timing logic check is performed with the first detection timestamp of the downstream grid to calculate the pollution diffusion impact range and generate a chain association list of responsible entities.

2. The method for tracing the source of groundwater pollution based on blockchain according to claim 1 is characterized in that: The hydrological dynamic chain verification tag includes the chain parameters of the infiltration rate, aquifer thickness, and flow angle parameters written into the hash chain storage structure, the pollutant concentration gradient difference, the dynamic diffusion threshold range, and the geographic coordinate offset verification result. The pollution spatiotemporal chain tag includes the pollution event timestamp, the geographic grid coding sorting result, and the migration direction and hydrological flow consistency verification mark. The node data credibility mark includes the blockchain monitoring node geographic coordinates, the distance attenuation relationship coefficient, and the concentration fluctuation range screening result. The responsible entity chain association list includes the candidate pollution source node release timestamp, the downstream grid first detection timestamp timing verification result, and the pollution diffusion impact range boundary parameter.

3. The method for tracing the source of groundwater pollution based on blockchain according to claim 1 is characterized in that: The specific steps of S1 are: S101: Collect the real-time infiltration rate, aquifer thickness, and flow angle of the groundwater sampling point, write the parameters and geographic coordinates into the blockchain chain storage structure at preset time intervals, and associate the previous block hash value; Generate real-time hydrological parameter hash chain; S102: extracting the pollutant concentration gradient difference between adjacent blocks in the real-time hydrological parameter hash chain, and calculating the dynamic diffusion threshold reference value in combination with the aquifer thickness parameter of the current block; Generate dynamic diffusion threshold coefficient; S103: Based on the dynamic diffusion threshold coefficient, the geographic coordinates of the current block and the previous block in the real-time hydrological parameter hash chain are called, and the offset between the two is compared with the threshold interval relationship of the dynamic diffusion threshold coefficient. If the offset is outside the threshold interval, the verification is determined to be abnormal, and a hydrological dynamic chain verification tag is generated.

4. The method for tracing the source of groundwater pollution based on blockchain according to claim 3 is characterized in that: The specific calculation formula for comparing the relationship between the offset and the threshold interval of the dynamic diffusion threshold coefficient is: Among them, D represents the Euclidean distance of the geographic coordinate offset between the current block and the previous block, k1 represents the lower threshold parameter of the dynamic diffusion threshold coefficient, k2 represents the upper threshold parameter, and H represents the hydrological dynamic chain verification parameter calculated based on the dynamic diffusion threshold coefficient and the offset.

5. The method for tracing the source of groundwater pollution based on blockchain according to claim 3 is characterized in that: The specific steps of S2 are: S201: Based on the hydrological dynamic chain verification tag, call the timestamp and geographic grid code of the pollution event in the target block, arrange the grid codes in ascending order according to the timestamp, establish a continuous distribution relationship of the grid codes based on the time of the event, and generate a spatiotemporal grid code sequence; S202: Extracting the migration direction parameter and the water flow direction parameter of each grid code in the spatiotemporal grid code sequence, calculating the angle difference between the two, and comparing them with the water flow direction deviation threshold preset by the hydrological dynamic chain verification tag, selecting the grid codes whose difference values meet the threshold, and generating a direction consistency verification coefficient; S203: Call the direction consistency check coefficient, extract the grid code with the check coefficient that meets the standard, connect them in series according to the time order of the spatiotemporal grid code sequence, superimpose the migration direction parameters to form a chain path, integrate the timestamp, grid code and migration direction parameters to construct a three-dimensional chain structure, and generate a contaminated spatiotemporal chain label.

6. The method for tracing the source of groundwater pollution based on blockchain according to claim 5 is characterized in that: The specific steps of S3 are: S301: Extract the pollution source code and timestamp based on the pollution spatiotemporal chain tag, call the pollutant concentration data and geographic coordinates of the blockchain monitoring node, associate the pollution source code and timestamp with the node concentration and location, and generate a node pollution parameter set; S302: Extracting pollution source codes, node geographic coordinates, and pollutant concentrations from the node pollution parameter set, matching pollutant types based on the pollution source codes, and calculating node concentration correction values based on the distance attenuation coefficient corresponding to the type. Based on the fluctuation data, a reasonable concentration range is defined to generate an attenuation-corrected concentration interval. S303: Compare the attenuation-corrected concentration interval with the actual concentration in the node pollution parameter set, screen node data that are logically self-consistent, assign a unique identifier based on the screening result, and generate a node data credibility identifier.

7. The method for tracing the source of groundwater pollution based on blockchain according to claim 6 is characterized in that: The specific steps of S4 are: S401: calling the node data credibility identifier, filtering nodes that meet the conditions based on the identifier, extracting the release timestamp, synchronously obtaining the first detection timestamp of the downstream grid, verifying the temporal logical relationship between the release timestamp and the detection timestamp, and generating a contaminated node set; S402: Based on the set of polluted nodes and the spatiotemporal parameters of the pollution diffusion path, the diffusion relationship between the node and the downstream grid is verified, and the coverage grid range that meets the diffusion logic is counted to generate the diffusion coverage range; S403: Integrate the polluted node set and the diffusion coverage, extract the node responsible entity identifier, build a chain association relationship between the responsible entity and the downstream grid according to the diffusion path level and release sequence, and output the responsible entity chain association list.

8. The method for tracing the source of groundwater pollution based on blockchain according to claim 7 is characterized in that: The specific calculation formula based on the diffusion path level and release timing is: Among them, α k represents the weight attenuation coefficient of the path level k, Δt represents the absolute value of the release timing difference between the responsible entity and the downstream grid node, τ d represents the pollution diffusion rate parameter, ρ n represents the density of polluted nodes, Δη k represents the gradient value of pollution concentration changing with level k, and β represents the time series compensation factor.

9. The method for tracing the source of groundwater pollution based on blockchain according to claim 7 is characterized in that: The method further comprises: S5: Based on the chain-linked list of responsible entities, call the compliance emission records of the same node in the cross-chain interoperability interface, verify the overlapping logic of the emission time and the pollution release time, and generate a multi-chain mutual authentication chain; The multi-chain mutual authentication evidence chain includes cross-chain interoperability interface node data, compliance emission record overlapping logic, and inter-chain mutual recognition identification of emission timestamps and pollution release timestamps; The specific steps of S5 are: S501: Based on the responsible party identifier and cross-chain association path in the responsible party chain association list, call the cross-chain interoperability interface to extract the emission time interval, single emission amount, and pollution type code of the emission record under the same node, the responsible party level and cross-chain node identifier in the association list, and integrate them to generate an emission record parameter set; S502: Based on the emission time interval in the emission record parameter set, extract the start time and end time of the pollution release time interval, compare the overlapping portion of the emission time interval with the pollution release time interval, calculate the ratio of the overlapping duration of the two to the total emission duration, filter out records with the ratio exceeding the compliance threshold, and generate a time overlap compliance record set; S503: Based on the time-overlapping compliance record set, associate the cross-chain identifier and emission record hash value of the same responsible entity in the multi-chain nodes, extract the timestamp consistency, node identifier matching degree, and hash value correlation in the mutual recognition parameters, calculate the cross-chain mutual recognition coefficient, and generate a multi-chain mutual recognition evidence chain.

10. The blockchain-based groundwater pollution tracing system is characterized by: According to any one of claims 1 to 9, the blockchain-based groundwater pollution tracing method comprises: The dynamic threshold verification module collects the real-time infiltration rate, aquifer thickness, and flow angle parameters of the groundwater sampling point, periodically writes them into the chain hash storage of the blockchain, combines the difference in pollutant concentration gradients between adjacent blocks and the aquifer thickness to calculate the dynamic diffusion threshold, compares the difference between the sampling point's geographic coordinate offset and the dynamic diffusion threshold, and generates a hydrological dynamic chain verification tag; The spatiotemporal flow direction verification module calls the hydrological dynamic chain verification tag, extracts the pollution event timestamp and grid code sequence, sorts the grid codes by timestamp, calculates the cosine fit of the angle between the pollutant migration direction and the water flow direction, and generates a pollution spatiotemporal chain tag; The node logic screening module calls the pollution spatiotemporal chain tag to obtain the pollutant concentration and geographic coordinate data of the monitoring node. Based on the Manhattan distance attenuation coefficient and concentration dispersion between the node and the pollution source, it screens the node data that meets the linear relationship between distance and concentration, and generates a node data credibility identifier; The responsibility time series association module calls the node data credibility identifier, extracts the release timestamp of the pollution source node and the first detection timestamp of the downstream grid, calculates the product of the timestamp difference and the diffusion rate, generates the diffusion impact sector area based on the grid coverage area, and generates a chain association list of responsible entities; The cross-chain evidence mutual verification module calls the chain association list of the responsible entity, traverses the compliance emission records of the target node in the cross-chain interface, verifies the intersection length of the emission record timestamp set and the pollution release timestamp, and generates a multi-chain mutual verification evidence chain.

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