Carbon flow graph generation method, system and device based on real-time big data space-time traceability and medium

Abstract

The invention relates to the technical field of carbon flow diagram generation, and discloses a carbon flow diagram generation method, system and device based on real-time big data space-time traceability and a medium, and the method comprises the steps: fusing multi-source real-time carbon related data collection, space-time correlation modeling and a block chain evidence storage mechanism; the defects of the existing carbon emission analysis method in the aspects of dynamics, relevance and credibility are effectively overcome. According to the method, the change characteristics of the carbon emission nodes in different time and space dimensions can be captured in real time, the carbon flow graph reflecting the space distribution characteristics and the time dynamic characteristics is generated, and a more accurate and reliable decision basis is provided for carbon emission management. Meanwhile, traceability, credibility and auditing performance of a carbon emission whole link are achieved through the block chain technology, the authenticity and integrity of carbon emission data are further improved, and powerful support is provided for carbon emission auditing and verification.

Description

Technical Field

[0001] This invention relates to the field of carbon flow map generation technology, and in particular to a carbon flow map generation method, system, device and medium based on real-time big data spatiotemporal tracing. Background Technology

[0002] Carbon emissions exhibit complex source structures and dynamic characteristics in industrial processes, building operations, and urban energy systems. To achieve effective monitoring and analysis of carbon emissions, existing technologies typically employ carbon accounting and visualization methods to model and display carbon flows.

[0003] Currently, common carbon emission analysis methods mainly include carbon accounting methods based on Life Cycle Assessment (LCA) and carbon emission maps based on Geographic Information Systems (GIS). The LCA method calculates carbon emissions at each stage by constructing the entire lifecycle of materials and energy flows for a product or system. However, it relies on static inventory data and preset emission factors, making it difficult to reflect real-time changes in carbon emissions caused by factors such as equipment start-up and shutdown, and load fluctuations during actual operation. Furthermore, it cannot dynamically track the transmission paths of carbon flows between different nodes. GIS carbon emission maps use spatial data to geographically map the total regional carbon emissions. While they possess spatial distribution display capabilities, they are typically based on statistical summary data (such as annual or monthly reports), resulting in low temporal resolution. They cannot capture the short-term dynamic characteristics of carbon emissions and lack the ability to model the causal relationships between carbon emission nodes.

[0004] Furthermore, existing carbon emission data management systems mostly employ centralized storage, lacking effective anti-tampering mechanisms during data collection, transmission, and storage. When auditing or verifying carbon emission pathways is required, ensuring the authenticity and integrity of the data is difficult. Although some systems have introduced data verification or logging mechanisms, end-to-end traceability and non-repudiation are still not achievable.

[0005] In recent years, technologies such as edge computing, streaming data processing, dynamic graph networks, and blockchain have been applied in the fields of energy and environmental monitoring. However, no technical solution has yet effectively integrated multi-source real-time carbon-related data acquisition, spatiotemporal correlation modeling, and a reliable evidence storage mechanism to simultaneously meet the technical requirements of carbon flow map generation for high timeliness (e.g., second-level updates), spatiotemporal dynamic expression (dual characteristics of spatial positioning and temporal evolution), and end-to-end reliable traceability (traceable, auditable, and tamper-proof). Therefore, there is an urgent need for a carbon flow map generation method that can integrate real-time big data processing, dynamic graph network modeling, and blockchain evidence storage to overcome the shortcomings of existing technologies in terms of dynamism, correlation, and reliability. Summary of the Invention

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, this invention provides a carbon flow map generation method, system, device, and medium based on real-time big data spatiotemporal tracing, which can solve the shortcomings of existing carbon emission analysis methods in terms of dynamism, correlation, and reliability.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a carbon flow map generation method based on real-time big data spatiotemporal tracing, comprising: Acquire multi-source carbon correlation data of the target area within a given time period, and perform a first preprocessing on the multi-source carbon correlation data; Based on the multi-source carbon correlation data after the first preprocessing, the spatiotemporal correlation between carbon emission nodes is analyzed. Based on the aforementioned spatiotemporal correlation, a dynamic graph network model of carbon flow is established; Using the aforementioned carbon flow dynamic graph network model, combined with edge computing and streaming computing, a carbon flow graph reflecting spatial distribution characteristics and temporal dynamic characteristics is generated; The key path data of the carbon flow map is stored in a reliable manner through blockchain, thereby achieving traceability, reliability and auditability of the entire carbon emission chain.

[0009] As a preferred embodiment of the carbon flow map generation method based on real-time big data spatiotemporal tracing described in this invention, the multi-source carbon-related data includes energy consumption, equipment operating status, carbon emission factors, geographical location information, and timestamps. The first preprocessing includes cleaning and normalizing the raw data, and adding a unique identifier and timestamp for the carbon emission node to the processed data to support subsequent spatiotemporal tracing and dynamic modeling.

[0010] As a preferred embodiment of the carbon flow map generation method based on real-time big data spatiotemporal tracing described in this invention, the spatiotemporal correlation includes spatial positioning correlation and temporal evolution correlation. The spatial location association is used to characterize the differences in the geographical distribution of different carbon emission nodes, including differences in regional energy structure; The time evolution correlation is used to characterize the dynamic behavior of carbon flow paths over time, including carbon flow changes caused by diurnal fluctuations in industrial energy consumption.

[0011] This preferred approach can more accurately capture the changing characteristics of carbon emission nodes across different time and spatial dimensions, thus providing stronger data support for the construction of the carbon flow dynamic graph network model. This helps improve the accuracy of the carbon flow graph in reflecting spatial distribution and temporal dynamic characteristics, enabling the generated carbon flow graph to more realistically and accurately present the actual situation of carbon emissions. This lays a good foundation for the subsequent realization of full-chain traceability, credibility, and auditability of carbon emissions, and ultimately provides a more powerful decision-making basis for carbon emission management, policy formulation, and energy conservation and emission reduction.

[0012] As a preferred embodiment of the carbon flow graph generation method based on real-time big data spatiotemporal tracing described in this invention, the construction process of the carbon flow dynamic graph network model includes: Carbon emission nodes are used as vertices of the graph network, and carbon flow transmission paths are used as edges of the graph network. The weight of the edge is determined by the causal strength of the carbon flow, and the vertex attributes include carbon concentration, geographic location and timestamp; The graph network structure is updated in real time using streaming computation.

[0013] As a preferred embodiment of the carbon flow map generation method based on real-time big data spatiotemporal tracing described in this invention, the step of reliably storing the critical path data of the carbon flow map through a blockchain includes: Data fingerprints are generated for key carbon emission nodes and carbon flow paths in the carbon flow graph; Write the data fingerprint, corresponding timestamp, spatial coordinates, and causal strength metadata into the blockchain; By leveraging the immutability of blockchain, we can ensure the credible traceability and post-event auditing of the entire carbon emission pathway.

[0014] As a preferred embodiment of the carbon flow map generation method based on real-time big data spatiotemporal tracing described in this invention, the method is applied to industrial park or urban carbon management scenarios. The multi-source carbon-related data is collected and preliminarily processed locally by edge computing devices deployed at carbon emission nodes. The pre-processed data is uploaded to the regional carbon management platform via a secure communication channel, where the platform performs dynamic carbon flow graph network modeling and carbon flow graph generation.

[0015] As a preferred embodiment of the carbon flow map generation method based on real-time big data spatiotemporal tracing described in this invention, the carbon flow map includes a carbon concentration heatmap, a carbon flow causal path map, and a time evolution animation. The carbon concentration range is divided into four intervals: [0, q 0.25 ), [q 0.25 q 0.5 ), [q0.5 q 0.75 ) and [q 0.75 [1], corresponding to carbon concentrations of <420ppm, 420–480ppm, 480–550ppm and >550ppm, respectively; Each carbon concentration range corresponds to a preset visualization color, transparency, and causal intensity, with the visualization colors being light blue, sky blue, light green, and red in that order.

[0016] Secondly, the present invention provides a carbon flow map generation system based on real-time big data spatiotemporal tracing, comprising: The data acquisition module is used to acquire multi-source carbon-related data for the target area within a given time period; The preprocessing module is used to perform a first preprocessing on the multi-source carbon correlation data; The correlation analysis module is used to analyze the spatiotemporal correlation between carbon emission nodes based on preprocessed data; The modeling module is used to establish a dynamic graph network model of carbon flow based on the spatiotemporal correlation. The carbon flow graph generation module is used to generate a carbon flow graph that reflects the spatial distribution and temporal dynamic characteristics by utilizing the carbon flow dynamic graph network model and combining edge computing and streaming computing. The blockchain evidence storage module is used to reliably store the critical path data of the carbon flow map through the blockchain, thereby achieving traceability, reliability, and auditability of the entire carbon emission chain.

[0017] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0018] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a carbon flow map generation method based on real-time big data spatiotemporal tracing. By integrating multi-source real-time carbon-related data acquisition, spatiotemporal correlation modeling, and blockchain evidence storage mechanisms, it effectively solves the shortcomings of existing carbon emission analysis methods in terms of dynamism, correlation, and reliability. This method can capture the changing characteristics of carbon emission nodes in different time and spatial dimensions in real time, generating a carbon flow map that reflects spatial distribution characteristics and temporal dynamic characteristics, providing a more accurate and reliable decision-making basis for carbon emission management. Simultaneously, by using blockchain technology to achieve traceability, reliability, and auditability of the entire carbon emission chain, it further enhances the authenticity and integrity of carbon emission data, providing strong support for carbon emission auditing and verification. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a carbon flow map generation method based on real-time big data spatiotemporal tracing, provided as an embodiment of the present invention.

[0022] Figure 2 This is an internal structure diagram of an electronic device for a carbon flow map generation method based on real-time big data spatiotemporal tracing, as provided in one embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] It should be noted in advance that the system mentioned in the embodiments as the subject of real-time operation refers to any system configured with this method.

[0025] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a carbon flow map generation method based on real-time big data spatiotemporal tracing, including: Existing technologies have several problems, such as insufficient dynamic performance, making it difficult to reflect changes in carbon emissions caused by factors such as equipment start-up and shutdown and load fluctuations in real time; lack of correlation modeling, making it impossible to effectively capture the spatiotemporal correlation between carbon emission nodes; and questionable data reliability, with centralized storage methods lacking effective anti-tampering mechanisms, making it difficult to ensure the authenticity and integrity of carbon emission data.

[0026] This invention provides a method that can effectively solve the problems mentioned above. The following sections will elaborate on how to implement this carbon flow map generation method based on real-time big data spatiotemporal tracing using multiple embodiments. Figure 1 A flowchart illustrating a carbon flow map generation method based on real-time big data spatiotemporal tracing is shown, including: S101, acquire multi-source carbon correlation data of the target area within a given time period, and perform first preprocessing on the multi-source carbon correlation data; In this embodiment of the invention, multi-source carbon-related data includes energy consumption, equipment operating status, carbon emission factors, geographic location information, and timestamps; The first preprocessing step includes cleaning and normalizing the raw data, and adding unique identifiers and timestamps for carbon emission nodes to the processed data to support subsequent spatiotemporal tracing and dynamic modeling.

[0027] In one optional implementation, raw data from various carbon emission sources within the target area can be acquired. For example, energy consumption can be collected in real time from electricity meters, gas meters, and heat meters in industrial parks; equipment operating status can be obtained from equipment sensors, such as boiler start / stop signals, fan speed, and production line load rate; and carbon emission factors corresponding to the energy type can be retrieved from national or industry carbon emission factor databases, such as 0.785 kg of carbon dioxide emission factor per kilowatt-hour of electricity. In one optional implementation, the precise geographical location information of carbon emission nodes can be obtained through BeiDou or GPS modules, for example, the latitude and longitude coordinates of a certain chimney are 120.123 degrees east longitude and 30.456 degrees north latitude; high-precision timestamps can be obtained through the built-in clock synchronization protocol of edge computing devices, for example, using the PTP precision time protocol to ensure that the time error of each node is less than 1 millisecond; In an optional implementation, the raw data can be cleaned, for example, by removing abnormal energy consumption values ​​caused by sensor malfunctions and correcting missing timestamps caused by communication interruptions; normalization processing can be performed, for example, by uniformly converting energy consumption data of different dimensions into standard coal equivalent; and a unique carbon emission node identifier can be added to each processed data record, for example, assigning a globally unique ID "BOILER-F3-2025" to Boiler No. 3 of a certain factory and binding it with a calibrated timestamp to support subsequent accurate source tracing of carbon flow paths and dynamic graph network modeling in the spatiotemporal dimension.

[0028] Among them, multi-source carbon-related data refers to structured or unstructured data that comes from different devices, systems or databases and is directly or indirectly related to carbon emissions. The first preprocessing refers to performing standardized operations such as data cleaning, unit normalization and label binding on the raw carbon-related data to form a high-quality dataset that can be used for spatiotemporal correlation analysis.

[0029] It should be noted that this step provides high-quality, structured, and spatiotemporally aligned foundational data for all subsequent analyses. By cleaning outliers, normalizing units, and adding unique identifiers and timestamps, the data is ensured to be consistent, complete, and temporally comparable, thereby supporting accurate spatiotemporal correlation analysis and dynamic modeling, and avoiding distortion of subsequent models due to noise or missing data in the original data.

[0030] S102, Based on the multi-source carbon correlation data after the first preprocessing, analyze the spatiotemporal correlation between carbon emission nodes; In embodiments of the present invention, spatiotemporal correlation includes spatial positioning correlation and temporal evolution correlation; Spatial location association is used to characterize the differences in the geographical distribution of different carbon emission nodes, including differences in regional energy structure; Temporal evolution correlation is used to characterize the dynamic behavior of carbon flow paths over time, including carbon flow changes caused by diurnal fluctuations in industrial energy consumption.

[0031] In an alternative implementation, a spatial adjacency matrix can be constructed based on the geographical location information of carbon emission nodes. For example, the inverse distance weighting method can be used to calculate the spatial association strength between any two nodes, with higher weights for closer nodes. Spatial location associations can be identified by combining regional energy structure data. For example, if an industrial park is mainly coal-fired while the adjacent area is mainly photovoltaic, then the two exhibit significant spatial heterogeneity in carbon flow characteristics. In one alternative implementation, carbon concentration and energy consumption data can be collected synchronously by a sensor network deployed in different geographical locations. For example, plant A is located upwind and uses natural gas, while plant B is located downwind and relies on diesel generators, thus forming a directional spatial carbon flow correlation. At the same time, a time sliding window can be established to segment and model the carbon flow sequence. For example, the time period can be divided into 15-minute intervals to analyze the changes in carbon emission intensity during periods such as morning peak and nighttime trough. In one alternative implementation, temporal evolution correlations can be identified. For example, a manufacturing company operates at high load from 8:00 to 20:00 every day, causing the carbon flow path to concentrate during this period, while the carbon flow significantly weakens or even stops after 22:00. Furthermore, by combining historical load curves with real-time operating status, for example, production line shutdowns on weekends cause the carbon flow path to exhibit periodic contraction and expansion in the time dimension, thus fully depicting the dynamic evolution pattern of the carbon flow path on the time axis.

[0032] Among them, spatial location correlation refers to the structural correlation formed in space by carbon emission nodes due to differences in geographical location, energy type or regional function, while temporal evolution correlation refers to the dynamic change pattern of carbon flow paths in the time dimension due to changes in production scheduling, energy consumption habits or external environment.

[0033] It should be noted that this step reveals the intrinsic connections between carbon emission nodes in terms of spatial distribution and temporal evolution, clarifying which nodes geographically influence each other and which nodes are causally driven over time. This relationship forms the logical basis for constructing a dynamic graph network model of carbon flows, giving the edges and weights in the graph model physical meaning and statistical basis, rather than simply providing connections.

[0034] S103, a dynamic graph network model of carbon flow is established based on spatiotemporal correlation; In this embodiment of the invention, the construction process of the carbon flow dynamic graph network model includes: Carbon emission nodes are used as vertices of the graph network, and carbon flow transmission paths are used as edges of the graph network. The weight of the edge is determined by the causal strength of the carbon flow, and the vertex attributes include carbon concentration, geographic location and timestamp; The graph network structure is updated in real time using streaming computation.

[0035] In an alternative implementation, each carbon emission node with a unique identifier can be mapped to a vertex in a graph network. For example, a power plant, a chemical reactor, or a regional power distribution transformer can all be used as independent vertices. The actual carbon flow transmission path can be modeled as directed edges connecting vertices. For example, the power transmission path from a coal-fired power plant to an industrial park corresponds to an edge pointing from the power plant vertex to the power-consuming equipment vertex in the park. In an alternative implementation, the weights of the edges are dynamically assigned based on the causal strength of the carbon flow. For example, the driving force of changes in power plant generation on carbon emissions in the park can be quantified by Granger causality test or information flow analysis, and the causal strength value can be used as the edge weight. In one optional implementation, vertex attributes integrate multi-source data in real time. For example, a vertex may contain the current carbon dioxide concentration sensor reading, latitude and longitude coordinates, and a timestamp accurate to the second. Newly generated carbon-related data streams are continuously received through a streaming computing engine. For example, Apache Flink or Spark Streaming processes data from edge devices in real time. When a new carbon emission node is detected to be online, the carbon concentration of an existing node changes abruptly, or the carbon flow path is interrupted, the corresponding vertices and edges are immediately added, deleted, or adjusted. For example, if a production line stops, the carbon concentration of its vertex will drop to zero and the weight of its associated edges will be reset to zero, thereby achieving millisecond-level dynamic updates of the graph network structure.

[0036] Among them, the carbon flow dynamic graph network model refers to a time-varying graph structure with carbon emission nodes as vertices and carbon flow transmission paths as edges, and its structure and properties are maintained through real-time streaming computation. It is used to accurately characterize the propagation and evolution of carbon flow in the spatial and temporal dimensions.

[0037] It should be noted that this step transforms the abstract spatiotemporal relationship into a computable and updatable graph structure, with vertices representing carbon emission entities and edges representing carbon flow paths, and assigning dynamic attributes. This model provides the core data structure and computational framework for generating carbon flow graphs, enabling the visualization results to not only present a static distribution but also reflect the causal transmission and real-time evolution of carbon flow.

[0038] S104 utilizes a carbon flow dynamic graph network model, combining edge computing and streaming computing, to generate a carbon flow graph that reflects spatial distribution characteristics and temporal dynamic characteristics. In one optional implementation, based on a carbon flow dynamic graph network model, edge computing devices are used to collect and preliminarily process multi-source carbon-related data in real time, and the carbon flow map is continuously updated through a streaming computing engine. After acquiring the first pre-processed data of carbon emission nodes, the regional carbon management platform can begin to systematically construct the carbon flow dynamic graph network model. During this process, a sensor network can be used to monitor information such as carbon concentration and energy consumption in the target area in real time at high frequency. The sensor network can quickly capture the changes in carbon emissions at different geographical locations over different time periods, obtaining a carbon flow map that reflects spatial distribution characteristics and temporal dynamic characteristics. By analyzing the carbon flow map and using spatiotemporal correlation algorithms, key carbon emission nodes can be accurately identified, such as high-energy-consuming enterprises and traffic-congested road sections. Furthermore, causal relationship analysis techniques can be used to accurately identify key factors affecting carbon emission intensity in the carbon flow path, such as the impact of industrial production activities or weather conditions on carbon emissions during specific periods.

[0039] Furthermore, in-depth analysis can be conducted on information such as the location, operational status, and environmental parameters of carbon emission nodes. For example, data analysis can determine the specific reasons for increased traffic carbon emissions in a particular region due to morning and evening rush hours. This provides comprehensive and detailed data support for developing targeted emission reduction measures.

[0040] Among them, carbon flow maps refer to a series of charts containing detailed information on carbon emissions in different time and space dimensions generated by real-time data collection of target areas during the carbon emission monitoring process. Key carbon emission nodes refer to locations or facilities that are currently generating significant carbon emissions or are about to significantly increase carbon emissions during the carbon emission process. Key factors refer to factors or conditions in the carbon flow path that have a significant impact on the total amount of carbon emissions and their spatiotemporal distribution.

[0041] In an embodiment of the present invention, the carbon flow graph includes a carbon concentration thermogram, a carbon flow causal path graph, and a time evolution animation; The carbon concentration range is divided into four intervals: [0, q 0.25 ), [q 0.25 q 0.5 ), [q 0.5 q 0.75) and [q 0.75 [1], corresponding to carbon concentrations of <420ppm, 420–480ppm, 480–550ppm and >550ppm, respectively; Each carbon concentration range corresponds to a preset visualization color, transparency, and causal intensity. The visualization colors are light blue, sky blue, light green, and red, respectively.

[0042] It should be noted that q here 0.25 q 0.5 q 0.75 These are the quartiles in the dataset; It's important to note that this step transforms the model into intuitive, interactive visualizations, such as heatmaps, causal path diagrams, and time-series animations, enabling managers to monitor carbon emission hotspots, propagation paths, and fluctuation patterns in real time. High-quality carbon flow maps not only support decision-making but also provide clear objects and structured data inputs for subsequent critical path identification and credible evidence preservation.

[0043] S105 uses blockchain to reliably store the critical path data of the carbon flow map, achieving traceability, reliability, and auditability across the entire carbon emission chain.

[0044] In this embodiment of the invention, the critical path data of the carbon flow graph is reliably stored using blockchain, including: Data fingerprints are generated for key carbon emission nodes and carbon flow paths in the carbon flow graph; Write the data fingerprint, corresponding timestamp, spatial coordinates, and causal strength metadata into the blockchain; By leveraging the immutability of blockchain, we can ensure the credible traceability and post-event auditing of the entire carbon emission pathway.

[0045] In one optional implementation, data fingerprints can be generated for key carbon emission nodes and carbon flow paths in the carbon flow graph. For example, the SHA-256 algorithm can be used to hash the dataset of each key carbon emission node to obtain a unique data fingerprint. The data fingerprint, corresponding timestamp, spatial coordinates, and causal strength metadata are written into the blockchain. For example, the above information can be automatically packaged into blocks and added to the blockchain through smart contracts. Through the immutability of the blockchain, the end-to-end credible traceability and post-event auditing of the carbon emission path can be ensured. For example, by utilizing the distributed ledger characteristics provided by blockchain technology, any attempt to modify historical data will be detected and rejected by other nodes in the network, thereby ensuring the authenticity and integrity of the data.

[0046] Among them, a data fingerprint is a fixed-length string generated by applying a hash function to a specific dataset, which can uniquely identify the contents of the dataset; a timestamp records the exact time when the data fingerprint was created, which is used to prove that an event did indeed occur at a certain point in time; spatial coordinates provide specific geographical location information of carbon emission nodes, which helps to accurately analyze the spatial distribution characteristics of carbon emissions; and causal strength metadata describes the strength of the causal relationship between nodes on the carbon flow path, which is crucial for understanding the propagation mechanism of carbon emissions.

[0047] In this embodiment of the invention, the method is applied to carbon management scenarios in industrial parks or cities; Multi-source carbon-related data are collected and initially processed locally through edge computing devices deployed at carbon emission nodes; The pre-processed data is uploaded to the regional carbon management platform via a secure communication channel, where the platform performs dynamic carbon flow graph network modeling and carbon flow graph generation.

[0048] In an alternative implementation, this method can be deployed in industrial parks or urban carbon management scenarios. For example, in a national-level industrial park, full coverage monitoring of carbon emission nodes of more than 100 enterprises can be implemented. Local data collection and preliminary processing can be performed by edge computing devices deployed at carbon emission nodes. For example, intelligent terminals integrating sensors and edge computing modules can be installed in boiler rooms, power distribution rooms, process exhaust ports, etc., to collect power consumption, gas flow, CO2 concentration data in real time and complete outlier removal and normalization. In one optional implementation, the pre-processed data is uploaded to the regional carbon management platform via a secure communication channel. For example, a dedicated communication link constructed using the national cryptographic SM4 encryption algorithm and TLS 1.3 protocol transmits data packets with unique node identifiers and timestamps to the municipal carbon management cloud platform. In one alternative implementation, the regional carbon management platform performs carbon flow dynamic graph network modeling and carbon flow graph generation. For example, the platform uses received multi-source data to construct a dynamic graph network with enterprises as vertices and energy transmission or material flow as edges, and generates a carbon flow graph that includes a carbon concentration heatmap, a causal path graph, and a time evolution animation.

[0049] Among them, industrial park or urban carbon management scenario refers to the application environment for carbon emission monitoring and management implemented in industrial clusters or urban administrative areas; edge computing device refers to embedded computing device deployed at carbon emission sources with local data collection, cleaning and preliminary analysis capabilities; secure communication channel refers to communication link that uses encryption and identity authentication mechanisms to ensure that data is not tampered with or leaked during transmission; and regional carbon management platform refers to centralized or distributed computing system responsible for aggregating, modeling and visualizing carbon flow data within the region.

[0050] It should be noted that this step leverages the immutability and distributed nature of blockchain to solidify the metadata of key nodes and paths in the carbon flow graph, forming a digital evidence chain with legal validity and audit value. This not only ensures the authenticity and integrity of the data generated by all the aforementioned steps but also provides a trusted infrastructure for applications such as carbon trading, carbon verification, and accountability, enhancing the credibility and compliance of the entire carbon management system.

[0051] In summary, this invention proposes a carbon flow map generation method based on real-time big data spatiotemporal tracing. By integrating multi-source real-time carbon-related data acquisition, spatiotemporal correlation modeling, and blockchain notarization mechanisms, it effectively addresses the shortcomings of existing carbon emission analysis methods in terms of dynamism, correlation, and reliability. This method can capture the changing characteristics of carbon emission nodes in different time and spatial dimensions in real time, generating a carbon flow map that reflects spatial distribution characteristics and temporal dynamics, providing a more accurate and reliable basis for carbon emission management decisions. Simultaneously, by leveraging blockchain technology to achieve traceability, reliability, and auditability across the entire carbon emission chain, it further enhances the authenticity and integrity of carbon emission data, providing strong support for carbon emission auditing and verification.

[0052] Example 2, based on the above examples, the specific implementation of the carbon flow map generation method based on real-time big data spatiotemporal tracing can be as follows: Real-time carbon data such as energy consumption and carbon dioxide concentration of park equipment are collected through IoT sensor networks deployed in scenarios such as buildings, transportation hubs, and industries. Furthermore, satellite remote sensing is used to obtain spatial data such as vegetation cover and land use change in the park, which can be used to estimate the carbon absorption of the ecosystem; Furthermore, data such as enterprise energy consumption reports, supply chain data, and traffic flow data can be used to improve upstream and downstream data.

[0053] Furthermore, blockchain technology is used to add blockchain tags to each piece of data and store carbon footprint data at each stage to ensure reliable data traceability.

[0054] Furthermore, by constructing a sliding window in the spatiotemporal dimension, robust statistics are used to identify and repair outliers, thereby suppressing noise interference with carbon flow data.

[0055] Furthermore, define the spatiotemporal window. This includes all sensor nodes within a radius r centered at spatial location s during the time interval [ Data within: ,in, For spatial distance, This represents the length of the time window.

[0056] Furthermore, robustness statistics are calculated, where: Robust location estimation (median): Robust Scale Estimation (MAD): MAD Furthermore, outlier detection involves setting a threshold k·MAD (usually k=3) to determine whether a data point is an outlier: Outlier Detection: Furthermore, outlier repair involves using median substitution or spatiotemporal interpolation to repair outliers. Furthermore, missing values ​​are imputed, and the spatiotemporal carbon flow data is represented as a three-dimensional tensor. Tensor decomposition is used to decompose the observation tensor containing missing values. Recovering the complete tensor Where T represents the time dimension (e.g., hours / day); S represents the spatial dimension (e.g., sensor nodes / grid); and F represents the feature dimension (e.g., CO2 concentration, energy consumption). Furthermore, Tucker decomposition is performed, decomposing the tensor into the product of the core tensor and each mode matrix: Where G represents the core tensor, capturing spatiotemporal feature interactions, and U... , , The factor matrix representing each mode, It is the rank parameter.

[0057] Furthermore, the specific operation for imputing missing values ​​is as follows: 1) Initialization: Assign 0 to missing positions and construct the observation tensor. ; 2) Tensor decomposition: Optimize the objective function using alternating least squares (ALS) to minimize the reconstruction error of the observed data. 3) Missing value prediction: Reconstruct the complete tensor using the core tensor and factor matrix obtained from the decomposition, and fill in the missing positions.

[0058] Furthermore, cross-device time series alignment addresses the issue of misalignment in time series caused by inconsistent sampling frequencies (e.g., second-level vs. minute-level) or clock deviations between different devices (such as sensors and meters). The DTW algorithm is used to achieve time series alignment by dynamically adjusting the mapping relationship between time points through the calculation of the shortest path distance between two time series, thus resolving the phase offset problem.

[0059] Furthermore, a distance matrix is ​​constructed, and the Euclidean distance between points in the time series X={x1, x2, ..., xm} and Y={y1, y2, ..., yn} is calculated to form matrix D. , where D(i,j)=|xi-yi|; Furthermore, we seek a path W = {w1, w2, ..., wK} from D(1, 1) to D(m, n) that satisfies: ① Boundary conditions: w1 = (1, 1), wK = (m, n); ② Step size constraint: the path can only move right, down, or diagonally; ③ Minimize cumulative distance. ; Furthermore, a time point mapping relationship between X and Y is established based on the optimal path W to achieve sequence alignment.

[0060] By combining the above technologies, the quality of carbon flow data can be effectively improved, laying the foundation for subsequent multi-scale carbon flow tracing, modeling, and visualization in the park.

[0061] Furthermore, Granger causality tests are used to mine statistical causal rules of carbon flow-related variables in the time lag and spatial proximity dimensions, and a carbon flow influencing factor network is constructed. The Granger causality rules are transformed into a dynamic Bayesian network (DBN) structure, and combined with real-time monitoring data, the carbon flow path is updated in real time and the uncertainty is quantified through probability propagation.

[0062] Furthermore, carbon flow-related data are abstracted into a spatiotemporal panel dataset {Ys, t, Xs', t'}; where Ys, t: target variables (such as carbon emissions) at spatial location s and time t; and Xs', t': potential influencing variables (such as energy consumption, traffic flow, and meteorological parameters in the vicinity). Spatial weight matrix: using inverse distance weighting method Ws, s'= Quantify the spatial dependence between nodes.

[0063] Furthermore, a spacetime Granger causality test is performed, in which: 1) Local causality (basic model) Null hypothesis H0: γ1=γ2=…=γk=0. Use the F-test to determine whether X is a Granger cause of Y.

[0064] 2) Spatiotemporal causality (extended model) Added spatial lag item The two F-tests were used to simultaneously verify local causality (γi) and spatial causality (γi). ).

[0065] Furthermore, rule extraction and filtering were performed to retain causal relationships with p < 0.05; Furthermore, an uncertainty propagation model for dynamic Bayesian networks (DBNs) is established, in which: For specific operations related to network structure construction, the following can be performed: 1) Node definition: ① Carbon emission source nodes: enterprises, transportation hubs, etc.; ② Influencing factor nodes: energy consumption, wind speed, traffic flow; ③ Carbon sink nodes: vegetation, carbon capture equipment.

[0066] 2) Edge connection: Directed connections between nodes are made according to Granger causality rules, such as adding a directed edge from "electricity consumption in Park B" to "carbon emissions in Park A". 3) Initialization of Conditional Probability Table (CPT): Based on historical data, statistically learn the conditional probabilities between nodes, such as: P(Increased emissions in Park A | Surge in electricity consumption in Park B) = 0.75; For specific operations related to real-time inference and path tracing, the following can be performed: 1) Observation update: Input real-time sensor data (such as CO2 concentration, equipment power) as evidence nodes into DBN; 2) Inference Algorithm: Particle filtering (PF) is used to process the nonlinear dynamic system, and posterior probability estimation is achieved by iteratively updating particle weights. ① Particle initialization: Randomly generate N particles to represent the carbon flow path hypothesis; ② Weight Update: Calculate the particle weight wi ∝ P(observation | particle state) based on the observation data. ③ Path estimation: Determine the current carbon flow path by weighted average or maximum weight particle.

[0067] 3) Quantification of uncertainty ① Probability Interval: Calculate the 95% confidence interval for key nodes in the carbon flow path. ② Entropy measurement: through node entropy Quantify local uncertainty.

[0068] Furthermore, the generation of dynamic carbon flow maps specifically includes the following steps: A particle system and a Lagrange trajectory model are established. Carbon particles are generated based on carbon emission source information, and carbon flow diffusion is represented by simulating particle motion.

[0069] Furthermore, the position update formula is as follows: The speed update formula is: ,in Let be the particle's position and velocity at time t. For time step, These represent wind force, turbulence, and gravity, respectively, with m being the particle mass.

[0070] Furthermore, a Lagrange trajectory model is established to track the trajectory of a single carbon particle in the flow field, and the equations of motion are solved. To obtain positional changes, numerical methods such as the Euler method are actually used for discrete solutions, such as... .

[0071] Furthermore, WebGL hardware-accelerated rendering and quantile rendering are used, where: WebGL rendering utilizes parallel computing on the GPU, processing graphics rendering through vertex shaders and fragment shaders. The vertex transformation formula is as follows: ,in To clip spatial coordinates, These are projection, view, and model matrix, respectively. These are the coordinates of the vertex in the local coordinate system.

[0072] Furthermore, quantile rendering divides the data into different intervals based on quantiles, assigning each interval a different visualization style to display the uncertainty of carbon flow. For example, carbon concentration data is divided into... The four intervals, each corresponding to a different color and transparency, visually present the data distribution.

[0073] Furthermore, the drawing process involves coordinate transformation in the vertex shader, color calculation in the fragment shader, drawing particle and trajectory graphics, and displaying the dynamic process of carbon flow diffusion in real time.

[0074] Example 3, referring to Figure 2 This embodiment also provides a carbon flow map generation system based on real-time big data spatiotemporal tracing, including: The data acquisition module is used to acquire multi-source carbon-related data for the target area within a given time period; The preprocessing module is used to perform the first preprocessing on multi-source carbon-related data; The correlation analysis module is used to analyze the spatiotemporal correlation between carbon emission nodes based on preprocessed data; The modeling module is used to build a dynamic graph network model of carbon flow based on spatiotemporal correlations; The carbon flow graph generation module is used to generate carbon flow graphs that reflect spatial distribution and temporal dynamic characteristics by utilizing a carbon flow dynamic graph network model and combining edge computing and streaming computing. The blockchain evidence storage module is used to reliably store the critical path data of the carbon flow map through the blockchain, thereby achieving traceability, reliability, and auditability of the entire carbon emission chain.

[0075] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0076] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as follows: Figure 2As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a carbon flow map generation method based on real-time big data spatiotemporal tracing. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0077] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps: Acquire multi-source carbon correlation data for the target area within a given time period, and perform a first preprocessing on the multi-source carbon correlation data; Based on the multi-source carbon correlation data after the first preprocessing, the spatiotemporal correlation between carbon emission nodes is analyzed. A dynamic graph network model of carbon flow is established based on spatiotemporal correlation. By utilizing a carbon flow dynamic graph network model and combining edge computing and streaming computing, a carbon flow graph reflecting spatial distribution characteristics and temporal dynamic characteristics is generated. By using blockchain to reliably store the critical path data of carbon flow maps, the traceability, reliability, and auditability of the entire carbon emission chain can be achieved.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0079] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0080] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for generating carbon flow maps based on real-time big data spatiotemporal tracing, characterized in that, include: Acquire multi-source carbon correlation data of the target area within a given time period, and perform a first preprocessing on the multi-source carbon correlation data; Based on the multi-source carbon correlation data after the first preprocessing, the spatiotemporal correlation between carbon emission nodes is analyzed. Based on the aforementioned spatiotemporal correlation, a dynamic graph network model of carbon flow is established; Using the aforementioned carbon flow dynamic graph network model, combined with edge computing and streaming computing, a carbon flow graph reflecting spatial distribution characteristics and temporal dynamic characteristics is generated; The critical path data of the carbon flow map is stored in a reliable manner through blockchain, thereby achieving traceability, reliability, and auditability of the entire carbon emission chain.

2. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 1, characterized in that, The multi-source carbon-related data includes energy consumption, equipment operating status, carbon emission factors, geographic location information, and timestamps; The first preprocessing includes cleaning and normalizing the raw data, and adding a unique identifier and timestamp for the carbon emission node to the processed data to support subsequent spatiotemporal tracing and dynamic modeling.

3. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 2, characterized in that, The spatiotemporal relationships include spatial positioning relationships and temporal evolution relationships; The spatial location association is used to characterize the differences in the geographical distribution of different carbon emission nodes, including differences in regional energy structure; The time evolution correlation is used to characterize the dynamic behavior of carbon flow paths over time, including carbon flow changes caused by diurnal fluctuations in industrial energy consumption.

4. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 3, characterized in that, The construction process of the carbon flow dynamic graph network model includes: Carbon emission nodes are used as vertices of the graph network, and carbon flow transmission paths are used as edges of the graph network. The weight of the edge is determined by the causal strength of the carbon flow, and the vertex attributes include carbon concentration, geographic location and timestamp; The graph network structure is updated in real time using streaming computation.

5. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 4, characterized in that, The step of storing the critical path data of the carbon flow graph in a trusted manner via blockchain includes: Data fingerprints are generated for key carbon emission nodes and carbon flow paths in the carbon flow graph; Write the data fingerprint, corresponding timestamp, spatial coordinates, and causal strength metadata into the blockchain; By leveraging the immutability of blockchain, we can ensure the credible traceability and post-event auditing of the entire carbon emission pathway.

6. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 5, characterized in that, The method is applied to carbon management scenarios in industrial parks or cities. The multi-source carbon-related data is collected and preliminarily processed locally by edge computing devices deployed at carbon emission nodes. The pre-processed data is uploaded to the regional carbon management platform via a secure communication channel, where the platform performs dynamic carbon flow graph network modeling and carbon flow graph generation.

7. The carbon flow map generation method based on real-time big data spatiotemporal tracing as described in claim 6, characterized in that, The carbon flow graph includes a carbon concentration thermogram, a carbon flow causal path graph, and a time evolution animation; The carbon concentration range is divided into four intervals: [0, q 0.25 ), [q 0.25 q 0.5 ), [q 0.5 q 0.75 ) and [q 0.75 [1], corresponding to carbon concentrations of <420ppm, 420–480ppm, 480–550ppm and >550ppm, respectively; Each carbon concentration range corresponds to a preset visualization color, transparency, and causal intensity, with the visualization colors being light blue, sky blue, light green, and red in that order.

8. A carbon flow map generation system based on real-time big data spatiotemporal tracing, employing the method described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire multi-source carbon-related data for the target area within a given time period; The preprocessing module is used to perform a first preprocessing on the multi-source carbon correlation data; The correlation analysis module is used to analyze the spatiotemporal correlation between carbon emission nodes based on preprocessed data; The modeling module is used to establish a dynamic graph network model of carbon flow based on the spatiotemporal correlation. The carbon flow graph generation module is used to generate a carbon flow graph that reflects the spatial distribution and temporal dynamic characteristics by utilizing the carbon flow dynamic graph network model and combining edge computing and streaming computing. The blockchain evidence storage module is used to reliably store the critical path data of the carbon flow map through the blockchain, thereby achieving traceability, reliability, and auditability of the entire carbon emission chain.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the carbon flow map generation method based on real-time big data spatiotemporal tracing as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the carbon flow map generation method based on real-time big data spatiotemporal tracing as described in any one of claims 1 to 7.

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