Blockchain-based carbon emission accounting management method and system for seawater desalination systems

Abstract

The present application provides a method and system for carbon emission accounting management of seawater desalination system based on blockchain. Among them, a carbon footprint factor library is constructed by integrating the carbon emission data of the whole life cycle of seawater desalination membrane materials, and the quantitative relationship of carbon emission is associated with the membrane type in the production stage; the graphene sensor array is used to monitor the instantaneous change rate of the reverse osmosis membrane flux and the fluctuation value of the salt retention efficiency in real time to estimate the amount of pollutant deposition; based on the membrane performance degradation data and the carbon footprint factor, the real-time carbon emission increment during operation is dynamically calculated; when the increment exceeds the threshold, the green telecom credit quota bound to the geographical location is automatically matched through the blockchain smart contract to trigger a targeted carbon compensation transaction; finally, the transaction information is encrypted and stored in the blockchain ledger to form a full-chain evidence covering production traceability, operation tracking and compensation verification, and written into the membrane component chip to achieve closed-loop management of carbon emissions. The technical solution provided by this application significantly improves the accuracy of carbon emission accounting of seawater desalination systems.

Description

Technical Field

[0001] The present application relates to the field of carbon emission accounting technology, and in particular to a blockchain-based seawater desalination system carbon emission accounting management method and system. Background Art

[0002] Carbon emissions management in desalination systems urgently requires an automated, closed-loop management solution covering production, operation, and compensation. In particular, addressing the dynamic coupling between membrane material production carbon emission factors and operational loss data is crucial to avoid accounting errors caused by data gaps. Furthermore, existing technologies lack effective monitoring of carbon emission fluctuations caused by real-time pollutant deposition, and compensation mechanisms struggle to precisely match green electricity resources to the equipment's geographic location.

[0003] Carbon emission monitoring solutions based on IoT sensors and lifecycle static databases are widely used. This solution deploys pressure and temperature sensors to collect membrane module operating parameters. Combined with a pre-defined static database of membrane material production carbon emissions, a linear growth model is established to predict carbon emission trends. When the transmembrane pressure differential or energy consumption exceeds a fixed threshold, carbon offsets are matched with regional green telecom credits through a centralized trading platform. The compensation is uniformly converted based on the grid's carbon emission factor.

[0004] However, this solution has significant flaws. First, the lack of a dynamic correlation model means that the fixed emission factor cannot reflect the impact of actual operating conditions on membrane material loss, resulting in errors in the calculation of incremental carbon emissions. Second, the carbon compensation trigger mechanism relies on a fixed energy consumption threshold, which cannot capture sudden changes in carbon emissions caused by rapid pollutant deposition, making it prone to compensation lags or overcompensation. Finally, Green Telecom's credit quota matching relies on geographic grid data from a centralized platform, lacking a device-level geolocation binding verification mechanism. Furthermore, compensation transaction records are stored in a centralized database, posing a risk of data tampering. Summary of the Invention

[0005] This application provides a blockchain-based seawater desalination system carbon emission accounting management method and system to solve the problem of insufficient accuracy of carbon emission accounting in the existing technology.

[0006] In the first aspect, this application provides a blockchain-based method for carbon emission accounting and management of seawater desalination systems, including:

[0007] Integrate carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a carbon footprint factor set, in which the carbon dioxide equivalent value per unit area in the production stage is matched with the corresponding carbon emission quantification relationship according to the membrane component type;

[0008] A graphene sensor array is fixed on the membrane surface of the reverse osmosis membrane module to collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and the amount of pollutant deposition on the membrane surface is determined based on the salt retention efficiency fluctuation value;

[0009] Dynamically calculate the real-time carbon emission increment due to membrane performance degradation based on the instantaneous rate of change of membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, combined with the operating time of the seawater desalination device and the influent salt concentration data;

[0010] When the real-time carbon emissions increase exceeds a preset emission increase threshold, the smart contract in the blockchain network automatically matches the renewable energy credits tied to the geographical location of the desalination plant, and generates and executes a targeted carbon offset transaction request based on the renewable energy credits;

[0011] The directed carbon compensation transaction request is encrypted and stored in the blockchain distributed ledger to form a full-chain evidence covering membrane material production traceability, operation carbon emission tracking and compensation transaction verification, and the unique identification code of the full-chain evidence is written into the membrane component identity chip.

[0012] Optionally, the dynamic calculation of the real-time carbon emission increment due to membrane performance degradation based on the instantaneous rate of change of the membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, combined with the operating time of the seawater desalination device and the influent salt concentration data, includes:

[0013] Extracting spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining it with the carbon emission coefficient of the membrane material degradation stage in the carbon footprint factor set, and generating a dynamic correlation baseline between the flux decay rate and carbon emissions;

[0014] Establishing a nonlinear response relationship between the influent salt concentration data and the instantaneous change rate of the membrane flux, correcting the spatial heterogeneity of the flux decay rate by the amount of pollutant deposition on the membrane surface, and correcting the weight distribution of the dynamic correlation baseline;

[0015] Based on the operating time data of the seawater desalination device, the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage in the carbon footprint factor set is calculated to generate an additional carbon emission correction value for membrane performance degradation;

[0016] The corrected weight distribution and the additional carbon emission correction value due to membrane performance degradation are integrated, and the dynamic impact factor of the influent salt concentration data on the membrane surface charge density is combined to dynamically calculate the real-time carbon emission increment due to membrane performance degradation.

[0017] Optionally, the calculation of the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage in the carbon footprint factor set based on the operating time data of the seawater desalination device to generate the additional carbon emission correction value for membrane performance degradation includes:

[0018] Extract the distribution of ambient temperature extremes and the frequency of water inlet pressure fluctuations from the operating time data of the seawater desalination device. Combined with the initial performance parameters of the anti-fouling layer in the factory inspection report of the membrane module, a set of dynamic influencing factors on the degradation rate of the membrane material anti-fouling layer is generated.

[0019] Based on the spatiotemporal distribution characteristics of the carbon emission coefficient during the transportation phase in the carbon footprint factor set, and by the correlation between the ambient temperature and humidity gradient in the transportation route and the hygroscopic expansion rate of the membrane material, a dynamic attenuation model of the carbon emission coefficient during the transportation phase and the initial performance parameters of the anti-pollution layer is established;

[0020] The nonlinear amplification coefficient of the membrane material crystal defect density on the degradation rate of the membrane material anti-fouling layer under high temperature and high salt environment is introduced, and combined with the dynamic influencing factor set, the spatiotemporal coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage is calculated;

[0021] Based on the spatiotemporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane assembly, the additional carbon emission correction value for membrane performance degradation is generated by multiplying the degradation rate of the membrane material anti-fouling layer by the attenuation of the carbon emission coefficient in the transportation stage.

[0022] Optionally, the carbon footprint factor set is formed by integrating the carbon emission-related data of the whole life cycle of the seawater desalination membrane material, wherein the carbon dioxide equivalent value per unit area in the production stage is matched with the corresponding carbon emission quantitative relationship according to the membrane module type, including:

[0023] Obtaining raw membrane material production data, extracting multi-dimensional carbon emission parameters such as the membrane material sintering temperature curve and chemical solvent consumption, and dividing the multi-dimensional carbon emission parameters into discrete carbon footprint units based on the porosity distribution characteristics and anti-pollution layer thickness parameters corresponding to the membrane component type;

[0024] For the production stage data in the discretized carbon footprint unit, a dynamic mapping rule is established between the membrane module type and the carbon dioxide equivalent value per unit area. The dynamic mapping rule corrects the carbon emission deviation between different membrane module batches by coupling the crystallinity gradient of the membrane material with the sintering energy consumption;

[0025] By integrating the ambient temperature and humidity sensor data during the transportation phase of the membrane material and the measured degradation rate data during the disposal phase, the discretized carbon footprint unit is expanded into a continuous carbon footprint factor covering the production, transportation, and disposal phases. The continuous carbon footprint factor generates an independent data index identifier based on the membrane module serial number and the dynamic mapping rule;

[0026] Based on the historical distribution data of salinity and temperature in the service environment of the seawater desalination membrane material and combined with the data index identifier, the continuous carbon footprint factor is corrected for service adaptability to generate a carbon footprint factor set including the physical property parameters of the membrane material and the environmental adaptability coefficient.

[0027] Optionally, fixing a graphene sensor array on the membrane surface of the reverse osmosis membrane assembly to collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and determining the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value, comprises:

[0028] Arranging a graphene sensor array in a honeycomb topology within a preset area on the active layer surface of a reverse osmosis membrane assembly, adjusting the spacing density of the graphene sensor array according to the flow channel distribution characteristics of the membrane assembly, and physically connecting the electrode leads of the graphene sensor array to the embedded signal acquisition module of the membrane assembly end cap;

[0029] Based on the adjusted spacing density, the graphene sensor array is used in a periodic scanning mode to synchronously capture the instantaneous change rate of membrane flux and the fluctuation value of salt retention efficiency, and the current signal and ion adsorption characteristic spectrum of the membrane surface microarea are synchronously collected in a single scanning cycle, and high-frequency noise in the signal is filtered out while retaining valid data associated with the characteristic frequency band of membrane fouling;

[0030] A dynamic correlation model of membrane surface pollutant deposition is constructed based on the temporal variation trend of the salt rejection efficiency fluctuation value, and the attenuation slope of the salt rejection efficiency fluctuation value is converted into a pollutant deposition thickness distribution value in combination with the membrane module surface roughness parameter and the operating pressure gradient data;

[0031] Based on the spatial heterogeneity distribution of the instantaneous change rate of the membrane flux and the distribution value of the pollutant deposition thickness, a three-dimensional thermal map of the pollutant accumulation hotspot area is constructed on the flow channel cross-section of the membrane surface, and the backwash cycle optimization parameters of the current operation stage are associated to determine the pollutant deposition amount on the membrane surface.

[0032] Optionally, when the real-time carbon emission increment exceeds a preset emission increment threshold, the smart contract in the blockchain network automatically matches the renewable energy credits bound to the geographical location of the desalination device, and generates and executes a targeted carbon compensation transaction request based on the renewable energy credits, including:

[0033] Preset emission increment thresholds and geographic location matching rules in the blockchain network to establish a grid-based binding relationship between the geographic location of the desalination plant and the renewable energy credit supplier database in the region;

[0034] receiving the operating status data of the seawater desalination device in real time, extracting the timestamp and geographic coordinate information of the real-time carbon emission increment, and verifying the spatial consistency of the geographic coordinate information and the grid binding relationship;

[0035] When the real-time carbon emission increment exceeds the emission increment threshold and the verification results are inconsistent, the renewable energy credit pool is retrieved according to the grid number corresponding to the geographic coordinate information, and the tradable credit weight is dynamically adjusted in combination with the current regional wind power forecast data and photovoltaic irradiance monitoring data;

[0036] A targeted carbon compensation transaction request including the incremental carbon emission compensation value, the timestamp, and the grid number is generated according to the tradable credit weight, the credit transfer is completed through the atomic exchange protocol of the blockchain smart contract, and the transaction execution result is written into the cross-chain distributed ledger.

[0037] Optionally, the dynamic mapping rule between the membrane module type and the carbon dioxide equivalent value per unit area is established, and the dynamic mapping rule corrects the carbon emission deviation between different membrane module batches by coupling the relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption, including:

[0038] Extracting historical sintering process data of membrane module production batches, combining it with the measured value of the membrane material crystallinity gradient, and generating a correlation between the membrane material crystallinity gradient and the sintering process parameters;

[0039] According to the correlation relationship, the porosity distribution characteristics and the anti-pollution layer thickness parameter, the energy consumption distribution in the historical sintering process data is divided into a high energy consumption interval and a low energy consumption interval, and a segmented mapping relationship between the membrane module type and the carbon dioxide equivalent value per unit area is established respectively;

[0040] Based on the actual sintering energy consumption data of the membrane module production batch, the carbon emission correction factor is calculated through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption;

[0041] The carbon emission correction coefficient is bound to the production serial code of the membrane module production batch, the carbon emission quantification relationship in the segmented mapping relationship is updated, and the carbon emission deviation between different membrane module batches is corrected.

[0042] Optionally, the dynamic correlation model of membrane surface pollutant deposition is constructed based on the time-domain variation trend of the salt rejection efficiency fluctuation value, and the attenuation slope of the salt rejection efficiency fluctuation value is converted into a pollutant deposition thickness distribution value in combination with the membrane module surface roughness parameter and the operating pressure gradient data, including:

[0043] Extracting the time domain variation data of the salt rejection efficiency fluctuation value, obtaining the initial change rate and the stable change rate of the attenuation slope, and combining the surface roughness parameters of the membrane module to divide the rapid accumulation stage and the slow accumulation stage of pollutant deposition;

[0044] According to the spatial distribution characteristics of the operating pressure gradient data, the membrane assembly surface is divided into high-pressure areas and low-pressure areas, and regional mapping relationships between the attenuation slope and the pollutant deposition thickness are established respectively;

[0045] Based on the influence of the membrane assembly surface roughness parameter on the attenuation slope, as well as the rapid accumulation stage and the slow accumulation stage, the calculated deposition thickness value in the regional mapping relationship is corrected to generate an initial distribution of pollutant deposition thickness;

[0046] The time domain variation trend of the attenuation slope is combined with the dynamic variation of the operating pressure gradient data to update the initial distribution of pollutant deposition thickness, and the attenuation slope is converted into a pollutant deposition thickness distribution value.

[0047] Optionally, the encrypted storage of the targeted carbon offset transaction request in a blockchain distributed ledger to form a full-chain evidence storage covering membrane material production traceability, operation carbon emission tracking, and offset transaction verification, and writing the unique identification code of the full-chain evidence storage into the membrane module identification chip includes:

[0048] Encrypting the directed carbon offset transaction request to generate an encrypted transaction data packet embedded with the membrane module identity code;

[0049] Writing the encrypted transaction data packet into the blockchain distributed ledger, establishing a chain association of membrane material production traceability, operation carbon emission tracking, and compensation transaction verification in the ledger, forming a full chain of evidence;

[0050] Perform distributed verification of the full-chain evidence based on the blockchain consensus mechanism to generate a unique identification code for the evidence containing the transaction hash value;

[0051] The unique identification code of the evidence is written into the membrane component identification chip through the physical interface, and the evidence writing timestamp and blockchain node verification information are recorded.

[0052] Secondly, this application provides a blockchain-based seawater desalination system carbon emission accounting and management system, including:

[0053] A matching module is used to integrate carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a set of carbon footprint factors, in which the carbon dioxide equivalent value per unit area during the production phase is matched to the corresponding carbon emission quantification relationship based on the membrane module type;

[0054] An acquisition module is used to fix a graphene sensor array on the membrane surface of the reverse osmosis membrane assembly, collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and determine the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value;

[0055] a calculation module for dynamically calculating the real-time incremental carbon emissions due to membrane performance degradation based on the instantaneous rate of change of the membrane flux, the amount of pollutant deposition on the membrane surface, and the set of carbon footprint factors, in combination with the operating time of the desalination device and the influent salt concentration data;

[0056] A generation module is configured to automatically match renewable energy credits tied to the geographical location of the desalination device through a smart contract in the blockchain network when the real-time carbon emission increment exceeds a preset emission increment threshold, and generate and execute a targeted carbon offset transaction request based on the renewable energy credits;

[0057] The storage module is used to encrypt and store the directed carbon compensation transaction request in a blockchain distributed ledger, forming a full-chain evidence covering membrane material production traceability, operation carbon emission tracking and compensation transaction verification, and write the unique identification code of the full-chain evidence into the membrane component identity chip.

[0058] In one embodiment of the present application, carbon footprint factor sets are formed by integrating carbon emission-related data from the entire life cycle of desalination membrane materials. The carbon dioxide equivalent per unit area during the production phase is matched to a corresponding carbon emission quantification relationship based on the membrane module type. A graphene sensor array is fixed to the membrane surface of the reverse osmosis membrane module to collect the instantaneous rate of change of membrane flux and the fluctuation of salt retention efficiency in real time. The amount of pollutant deposition on the membrane surface is determined based on the fluctuation of the salt retention efficiency. The real-time incremental carbon emissions due to membrane performance degradation are dynamically calculated based on the instantaneous rate of change of membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, combined with the desalination unit's operating time and influent salt concentration data. When the real-time incremental carbon emissions exceed a preset incremental emissions threshold, a smart contract in the blockchain network automatically matches renewable energy credits tied to the desalination unit's geographic location, generates and executes a targeted carbon offset transaction request based on the renewable energy credits, and encrypts and stores the targeted carbon offset transaction request in the blockchain distributed ledger, forming a fully chained evidence system that covers membrane material production traceability, operational carbon emissions tracking, and offset transaction verification. The unique identification code of the fully chained evidence system is then written into the membrane module identification chip.

[0059] The technical solution of this application has the following beneficial effects:

[0060] Achieve typological matching of carbon emission coefficients in the production, transportation, and degradation stages of membrane materials to ensure that carbon accounting covers key nodes throughout the entire life cycle; accurately quantify the dynamic impact of pollutant deposition on membrane performance degradation through the simultaneous collection of membrane flux instantaneous rate and salt retention efficiency fluctuation values; establish a real-time mapping relationship between membrane performance attenuation and carbon emissions based on operating time, influent salt concentration, and carbon footprint factor to improve the timeliness of incremental calculations; automatically match green telecom credit quotas based on geographic location binding to ensure a strong correlation between carbon compensation behavior and the actual operating environment of the equipment; achieve tamper-proof carbon footprint data and full-process traceability through encrypted storage of blockchain distributed ledgers and solidification of chip identification codes.

[0061] Furthermore, by extracting the dynamic correlation between the spatial heterogeneity distribution data of membrane flux and the carbon footprint factor, and combining the nonlinear response correction of salt concentration to flux attenuation, a weighted optimization model for pollutant deposition to the carbon emission baseline was established. The coupling degree of the membrane anti-fouling layer degradation rate and the transport carbon emission coefficient was simultaneously integrated, and the dynamic impact factor of the influent salt concentration on the membrane surface charge was superimposed to construct a multi-dimensional real-time carbon emission increment calculation framework. Based on the weight correction of membrane flux spatial heterogeneity and pollutant deposition, dynamic optimization of the carbon emission baseline was achieved. The additional carbon emissions caused by membrane material loss were quantified through coupled modeling of anti-fouling layer degradation and transport carbon emissions. Combined with the real-time impact of salt concentration on charge density, a multi-factor linkage mechanism for accurate carbon emission increment accounting was formed, ultimately achieving the effect of reducing the error rate of carbon emission tracking throughout the life cycle and accurately matching compensation behavior with geographical green electricity resources.

[0062] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0064] Figure 1 A flowchart of a blockchain-based seawater desalination system carbon emission accounting and management method provided by this application is shown;

[0065] Figure 2 A schematic diagram of the structure of a blockchain-based seawater desalination system carbon emission accounting and management system provided by this application is shown. DETAILED DESCRIPTION

[0066] In order to enable people skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0067] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0068] By integrating the carbon emission factor data of the entire life cycle of membrane material production, transportation, and degradation, a dynamic mapping relationship between membrane component type and carbon footprint is established; a graphene sensor array is integrated on the surface of the reverse osmosis membrane, and its high sensitivity is used to capture the instantaneous fluctuations of membrane flux and changes in salt retention efficiency in real time, and the degree of membrane performance degradation is quantified through a correlation model between the salt retention efficiency fluctuation value and the pollutant deposition amount; a multi-parameter dynamic calculation model is constructed based on the operating time, influent salt concentration and carbon footprint factor set, and performance degradation indicators such as membrane flux attenuation rate and anti-pollution layer degradation are converted into real-time carbon emission increments; a blockchain smart contract trigger mechanism is designed to achieve automatic carbon compensation when the carbon emission increment exceeds the standard by binding the equipment's geographical location with the Green Telecom credit quota; finally, the blockchain distributed ledger is used to encrypt and store compensation transaction data, and the chip-level identification code is used to achieve full-chain carbon footprint traceability, forming a full-link carbon emission management closed loop covering "data collection, dynamic accounting, intelligent compensation, and trusted evidence storage."

[0069] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0070] Figure 1 The present invention provides a flowchart of a blockchain-based seawater desalination system carbon emission accounting and management method, as shown in FIG. Figure 1 As shown, the method includes:

[0071] 101. Integrate carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a carbon footprint factor set, in which the carbon dioxide equivalent value per unit area during the production phase is matched to the corresponding carbon emission quantification relationship based on the membrane module type;

[0072] In this step, the carbon footprint factor set refers to the carbon emission quantification parameter system constructed for the entire life cycle of seawater desalination membrane materials (production, transportation, operation, and disposal), which includes the physical property parameters and environmental correction factors associated with unit area / volume carbon emissions of membrane components at different stages.

[0073] In the embodiment of the present application, a multidimensional carbon emission parameter space is constructed through a life cycle assessment (LCA) model, and a random forest algorithm is used to perform feature importance analysis on production data such as the membrane material sintering temperature curve and the residual amount of chemical solvents, so as to screen out process parameters that are strongly correlated with carbon emissions. The discrete temperature and humidity data of the transportation stage and the measured values ​​of the degradation rate in the disposal stage are multi-dimensionally interpolated using hypercube sampling technology to generate a continuous carbon footprint factor surface. For different types of membrane components, the porosity distribution characteristics and the quantitative relationship of carbon emissions are matched by the Bayesian optimization algorithm, and finally a set of carbon footprint factors including the physical properties of the membrane material and the environmental adaptability coefficient is formed. The carbon dioxide equivalent value per unit area in the production stage is calculated by the energy consumption regression model of the high-temperature sintering process, and the input parameters include the thermal efficiency curve of the sintering furnace and the crystallinity gradient data of the membrane material.

[0074] Consider a desalination plant using DuPont FilmTec SW30XLE-400 reverse osmosis membrane modules. The production phase sintering process data includes a three-stage temperature profile (heating rate 15°C / min, peak temperature 372°C ± 3°C, hold time 3.2 hours). A random forest algorithm was used to feature-screen the membrane material's crystallinity gradient data (XRD half-peak width 0.32°), identifying the sintering furnace thermal efficiency coefficient (0.68) as a key carbon emission factor. Hypercube sampling was used to perform a three-dimensional interpolation of transportation phase temperature and humidity data (42% of the duration of humidity >90% during sea transportation) and disposal phase biodegradation experimental data (1.2% annual average mass loss rate under 30°C seawater immersion) to generate a set of carbon footprint factors. The resulting production phase carbon emission value corresponding to the membrane module's 0.25μm porosity was 1.35kg CO₂e / m².

[0075] 102. Fixing a graphene sensor array on the membrane surface of the reverse osmosis membrane assembly to collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and determining the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value;

[0076] In this step, the graphene sensor array is a sensing network composed of graphene field-effect transistors arranged in a honeycomb pattern. By monitoring the changes in current impedance in micro-areas on the membrane surface and the characteristic spectrum of ion adsorption, it captures the spatial heterogeneity data of membrane flux attenuation rate and salt retention efficiency fluctuation in real time.

[0077] In this embodiment, graphene sensing elements were fabricated on the surface of a polyamide active layer using a chemical vapor deposition (CVD) process. The spacing between the sensing nodes was adjusted (adjustable from 200 to 500 μm) based on the results of flow channel fluid dynamics simulations. The instantaneous rate of change of the membrane flux was acquired using time-resolved impedance spectroscopy (TRIS), with a sampling frequency of 10 kHz to capture nanosecond-level flux fluctuations. Salt retention efficiency fluctuations were determined through in situ Raman spectroscopy, with the integrated intensity calculated for the characteristic peak of Cl⁻ ion adsorption on the membrane surface (wavenumber 258 cm⁻¹). A wavelet packet decomposition algorithm was used to filter out high-frequency mechanical vibration noise (>1 MHz), retaining the characteristic contamination signals in the 0.1-10 kHz frequency band. A nonlinear mapping model between salt retention efficiency fluctuations and contaminant deposition thickness was established using a convolutional neural network (CNN). The input layer contained a 16-channel spatiotemporal feature tensor. The amount of contaminant deposition on the membrane surface was determined based on the salt retention efficiency fluctuations.

[0078] For example, continuing with the previous example, a 128-channel graphene sensor array was fabricated on the FilmTec membrane using a mask-assisted CVD process. The cell pitch was adjusted to 250-480μm based on the vortex intensity distribution of the flow channel. Time-resolved impedance spectroscopy (sampling rate 12.8kHz) was used to capture transient flux changes. A baseline flux value of 32 L / m² / h was detected at an inlet pressure of 6.5 MPa, with a peak-to-peak fluctuation of ±8%. A confocal Raman spectroscopy system (laser wavelength 785nm) was used to capture the characteristic double peaks (258 cm⁻¹ and 432 cm⁻¹) of Cl⁻ ions on the membrane surface. Wavelet packet decomposition was used to filter out pump vibration noise above 1.2 MHz. The processed spectral data was fed into a pre-trained 3D-ResNet model (input dimensions 16×16×64), which output a contaminant deposition rate of 5.2 μm on the northwest quadrant of the membrane surface.

[0079] 103. Dynamically calculate the real-time carbon emission increment due to membrane performance degradation based on the instantaneous rate of change of membrane flux, the amount of pollutant deposition on the membrane surface, and the set of carbon footprint factors, combined with the operating time of the seawater desalination device and the inlet salt concentration data;

[0080] In this step, the real-time carbon emission increment refers to the increase in carbon dioxide equivalent corresponding to the increase in unit water production energy consumption of the reverse osmosis system due to the deterioration of membrane component performance (including pollution, material degradation, etc.).

[0081] In this embodiment, an extended Kalman filter (EKF) is used to fuse multi-source heterogeneous data. The pollutant deposition thickness distribution data (spatial resolution 50 μm) output from step 102 is subjected to a tensor product operation with the degradation stage coefficients in the carbon footprint factor set. An asymmetric hyperbolic tangent function is used to describe the nonlinear effect of influent salt concentration on membrane osmotic pressure, with the parameters calibrated through high-pressure nuclear magnetic resonance (HP-NMR) experiments. A fractional-order differential equation is introduced to construct an anti-pollution layer degradation model, where the time fractional order α is dynamically adjusted based on operating time data (α = 0.83 for >5000 h). Finally, a probability density distribution of carbon emission increments is generated through Monte Carlo importance sampling, and the upper limit of the 95% confidence interval is selected as the output value of the real-time carbon emission increment.

[0082] For example, continuing with the previous example, after the device has accumulated 7200 hours of operation, the extended Kalman filter integrates the following data: the transport phase coefficient of 0.018 kg CO₂e / (m⋅h) in the carbon footprint factor set, the 5.2 μm sediment thickness output from step 102, and the inlet salt concentration of 41,000 ppm. The dynamic correction term is calculated using the salt concentration and osmotic pressure curve calibrated by high-pressure nuclear magnetic resonance (nonlinear coefficient γ = 1.73). The degradation of the antifouling layer is simulated using a fractional differential equation (α = 0.79), combined with Monte Carlo importance sampling (5000 iterations) to generate a probability distribution of carbon emission increments. The final output is a real-time carbon emission increment of 3.1 kg CO₂e / h, of which membrane fouling contributes 62% and material degradation contributes 38%.

[0083] 104. When the real-time carbon emissions increment exceeds a preset emission increment threshold, the smart contract in the blockchain network automatically matches the renewable energy credits tied to the geographical location of the desalination plant, and generates and executes a targeted carbon offset transaction request based on the renewable energy credits;

[0084] In this step, the targeted carbon compensation transaction request contains standardized transaction instructions for the carbon emission excess value, compensation timestamp and geographic grid code, which are used to accurately purchase renewable energy credits in a specific area to offset excess carbon emissions.

[0085] In this embodiment of the present application, a Delaunay triangulation model of the geographic grid is constructed, and the GPS coordinates of the desalination plant are mapped to 500m×500m grid cells. An improved R*-tree index structure is used to store regional wind power / photovoltaic output forecast data, and the optimal renewable energy credit is retrieved using an approximate nearest neighbor (ANN) algorithm. The smart contract uses zero-knowledge proofs (zk-SNARKs) to verify the spatiotemporal validity of incremental carbon emission data, and an atomic swap protocol is used to synchronize on-chain and off-chain data. An attention mechanism is introduced into the credit weight calculation to dynamically correct wind power forecast errors (weight reduced to 0.6 when RMSE > 15%) and photovoltaic irradiance monitoring delays (weight reduced to 0.4 when > 5 minutes). Finally, a targeted carbon compensation transaction request is generated and executed.

[0086] For example, continuing with the previous example, the blockchain smart contract determined that the device was located in the Fujairah grid in the UAE (code AE-FJ17). Using a modified R*-tree index, it retrieved the remaining wind power credit quota for that day at 58MWh and photovoltaic credit at 92MWh. The attention mechanism dynamically adjusted the credit weights based on weather forecast data (wind speed forecast error RMSE = 12.7%): wind power weighted 0.68, photovoltaic weighted 0.89. After verifying the validity of the incremental carbon emissions data using a zero-knowledge proof, a targeted carbon offset transaction request was generated and executed to purchase 4.8MWh of wind power credit and 3.2MWh of photovoltaic credit. The transaction hash was 0x4d9a...e7f1, with on-chain confirmation latency of <1.2 seconds.

[0087] 105. Encrypt and store the targeted carbon compensation transaction request in a blockchain distributed ledger to form a full-chain evidence storage covering membrane material production traceability, operation carbon emission tracking and compensation transaction verification, and write the unique identification code of the full-chain evidence storage into the membrane component identification chip.

[0088] In this step, full-chain evidence storage refers to an unalterable data chain that records the complete traceability of membrane material production, carbon emissions tracking logs during operation, and carbon offset transaction certificates in the blockchain distributed ledger. The unique identification code is an encrypted string generated by combining the blockchain hash value, the membrane module serial number, and the transaction timestamp. It is permanently stored in the membrane module's identification chip through physical burning.

[0089] In this embodiment, the IPFS protocol is used to shard and store raw carbon footprint data (shard size 256KB), and a sidechain is constructed using the Plasma framework to store high-frequency sensor data. The unique identification code for the certificate is generated using the Keccak-256 algorithm and includes the membrane module serial number (16 bits), blockchain height (64 bits), and compressed sensor fingerprint (128 bits). The physical writing process uses femtosecond laser ablation technology to form a micron-scale QR code array on the surface of a silicon nitride chip. During reading, the certificate information is analyzed using a confocal Raman microscope (wavelength 532nm). Cross-chain verification uses a light node SPV protocol, which quickly locates the target transaction through a Bloom filter (false positive rate <0.1%) to form a full-chain certificate.

[0090] For example, continuing with the previous example, targeted carbon offset transaction data is stored in IPFS shards (CID: bafkreiabc...vq). Using a Plasma sidechain architecture, 1,200 sensor data points per second are compressed into a Merkle root hash. The unique identifier "SW30XLE-FJ17-0x4d9a" is inscribed onto the surface of the membrane module chip using a femtosecond laser micromachining system (pulse energy 0.8mJ, wavelength 1030nm), forming a 2.5μm-deep QR code array. During a third-party audit, a terahertz time-domain spectrometer (resolution 6μm) is used to read the chip data. Cross-chain verification is completed within 1.8 seconds using a light-node SPV protocol. The resulting fully chained attestation includes the production batch number, cumulative carbon emissions over 7,200 hours of operation (22.3t CO2e), and a validation flag for the offset transaction.

[0091] Steps 101-105 utilize five core technologies: carbon footprint factor set construction, graphene sensor array deployment, dynamic incremental model calculation, blockchain smart contract execution, and full-chain evidence storage. This breakthrough dynamically links the physical degradation mechanisms of membrane materials with virtual carbon data, resolving core issues inherent in traditional approaches, such as the disconnect between static databases and dynamic operations, insufficient data credibility, and lagging compensation mechanisms. This provides an innovative technical approach for carbon footprint management in high-energy-consuming industrial facilities.

[0092] To address the disconnect between dynamic performance degradation and the static carbon factor library in carbon emission accounting for seawater desalination systems, and to further improve the temporal and spatial resolution accuracy of carbon emission increment calculations and the coupling of physical mechanisms, an innovative approach based on the deep fusion of multi-source heterogeneous data is proposed. In some embodiments, the real-time carbon emission increment caused by membrane performance degradation is dynamically calculated based on the instantaneous rate of change of membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, combined with the operating time of the seawater desalination device and the influent salt concentration data, including:

[0093] 201. Extracting spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining it with the carbon emission coefficient of the membrane material degradation stage in the carbon footprint factor set, and generating a dynamic correlation baseline between the flux decay rate and carbon emissions;

[0094] In step 201, the spatial heterogeneity distribution data refers to the non-uniform distribution characteristic data of the instantaneous change rate of the membrane flux in different flow channel areas on the reverse osmosis membrane surface, including the spatial difference value of the flux attenuation gradient.

[0095] The dynamic correlation baseline is a carbon emission calculation benchmark curve generated by coupling the flux decay rate and the carbon footprint factor, which reflects the carbon emission reference value under the initial state of membrane performance.

[0096] In this embodiment, a random forest algorithm was used to rank the spatiotemporal membrane flux data collected by a graphene sensor array based on feature importance, identifying spatial heterogeneity indicators strongly correlated with carbon emissions (e.g., regions with a standard deviation of the flux decay gradient greater than 15%). A nonlinear mapping surface was constructed using Gaussian process regression to map the degradation stage coefficient (e.g., 0.025 kg CO₂e / (m²·h)) and flux decay rate within a carbon footprint factor set. Input parameters included the membrane module porosity distribution (0.1-0.3 μm) and the operating pressure fluctuation range (5-8 MPa). Tensor decomposition was used to reduce the multidimensional data to a three-dimensional principal component space, generating a dynamic correlation baseline represented by a weight matrix (16×16 dimensions), with the flux decay rate accounting for 65% and the carbon footprint factor accounting for 35%.

[0097] 202. Establish a nonlinear response relationship between the influent salt concentration data and the instantaneous change rate of the membrane flux, correct the spatial heterogeneity of the flux decay rate by the amount of pollutant deposition on the membrane surface, and correct the weight distribution of the dynamic correlation baseline;

[0098] In step 202, the nonlinear response relationship refers to the non-proportional relationship between the change in influent salt concentration and the instantaneous rate of membrane flux, which is manifested as a salt concentration threshold effect.

[0099] In this example, a topological relationship model between salt concentration and flux attenuation was constructed using a graph convolutional network (GCN). Node features included salt concentration sampling values ​​(at 10-minute intervals), membrane surface flow channel geometry (curvature radius 0.5-2 mm), and contaminant deposition thickness (0-10 μm). An attention mechanism dynamically assigned flux attenuation correction coefficients (weights 0.3-0.7) for different salt concentration ranges (e.g., 30,000-40,000 ppm). A non-local means filtering algorithm was used to remove high-frequency noise (frequency range >100 Hz) from spatially heterogeneous data, while retaining low-frequency signatures associated with contaminant deposition (frequency range 1-10 Hz). Finally, a generative adversarial network (GAN) was used to modify the weight distribution of the dynamic correlation baseline, increasing the weight of highly contaminated areas (deposition >5 μm) to 1.8 times the original value.

[0100] 203. Based on the operating time data of the seawater desalination device, calculate the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage in the carbon footprint factor set, and generate an additional carbon emission correction value for membrane performance degradation;

[0101] In step 203, the coupling degree represents the synergistic effect between the rate of decrease in the thickness of the membrane material's anti-fouling layer and the rate of change in the carbon emission coefficient during the transportation phase. The additional carbon emission correction value due to membrane performance degradation is the incremental portion of the carbon emission value exceeding the baseline value due to membrane performance degradation and is quantified and generated through the coupling degree.

[0102] In this example, a long short-term memory (LSTM) network was used to analyze the degradation trend of the anti-fouling layer in long-term operating data (>5000 hours). Input features included ambient temperature extremes (25-45°C), inlet pH fluctuations (6.8-7.5), and backwash cycles (4-6 hours). A particle swarm optimization (PSO) algorithm was used to calculate the dynamic coupling between the carbon emission coefficient and the degradation rate during the transport phase, subject to constraints such as transport distance (a 20% increase in coupling for >2000 km) and ambient humidity (a 15% decrease in coupling for >80%). A Monte Carlo tree search (MCTS) algorithm was used to generate a probability distribution of correction values, with the 95th percentile selected as the output value for the additional carbon emission correction for membrane performance degradation.

[0103] 204. The corrected weight distribution and the additional carbon emission correction value due to membrane performance degradation are integrated, and the dynamic impact factor of the influent salt concentration data on the membrane surface charge density is combined to dynamically calculate the real-time carbon emission increment due to membrane performance degradation.

[0104] In step 204, the dynamic impact factor reflects the dynamic adjustment coefficient of the effect of changes in influent salt concentration on the membrane surface charge density (e.g., -30mV to -50mV). The real-time carbon emission increment is the final carbon emission excess value output after integrating the weight distribution correction value and the additional correction value.

[0105] In this embodiment, a multi-physics coupling model was constructed to spatially and temporally align charge density data (measured by a zeta potential meter) with influent salt concentration data. Finite element analysis (FEA) was then used to calculate the adsorption energy gradient (0.5-2.8 eV) of salt ions (Na⁺ and Cl⁻) on the membrane surface. A Bayesian optimization algorithm was used to dynamically adjust the weight distribution and the fusion ratio of the additional correction value (55% vs. 45%), subject to constraints including the membrane material dielectric constant (2.5-3.2) and operating voltage fluctuation (24V ± 5%). Finally, a genetic algorithm (GA) was used to globally optimize the output of the real-time incremental carbon emissions due to membrane performance degradation. The fitness function included a carbon emission increment threshold constraint (e.g., ≤3.5 kg CO₂e / h) and an energy cost weight (0.6).

[0106] Here's a specific example:

[0107] A SWRO desalination plant in the Middle East uses SWC5 membrane modules (initial flux 35 L / m² / h, anti-fouling layer thickness 120 μm). After 6500 hours of operation, the following steps were performed: First, according to step 201, the graphene sensor detected a flux attenuation gradient of 22% (baseline value ±8%) in the northeast quadrant of the membrane surface. Combined with the degradation coefficient of 0.028 kg CO₂e / (m²·h) in the carbon footprint factor, a dynamic correlation baseline value of 2.1 kg CO₂e / h was generated. According to step 202, the inlet salt concentration increased to 42,000 ppm. The GCN model outputted a salt concentration correction factor of 0.68, and the weight of the 5.8 μm region of pollutant deposition increased by 1.7 times, raising the baseline value to 2.9 kg CO₂e / h after correction. According to step 203, the LSTM predicted anti-fouling layer degradation rate was 0.025 μm / h. The PSO calculation had a coupling degree of 1.32 with the transport phase coefficient (0.021 kg CO₂e / km), generating an additional correction value of 0.8 kg CO₂e / h. CO2e / h; finally, according to step 204, the charge density dynamic factor is calculated to be 1.15 (when the salt concentration is >40000ppm), and the Bayesian optimization fusion weights are used to output a real-time carbon emission increment of 3.5kg CO2e / h.

[0108] Steps 201-204 achieve millimeter-level spatial resolution and minute-level dynamic updates of carbon emission increments in the desalination system through four-order innovations in spatial heterogeneity analysis, nonlinear response modeling, degradation and transport coupling calculation, and multi-physics field fusion optimization. This solves the problems of linear assumption errors in membrane performance degradation and carbon emissions, delayed response to salt concentration mutations, and cross-stage data silos in traditional methods, providing industrial-grade desalination facilities with a closed-loop carbon accounting system that is linked to the entire life cycle and real-time operation.

[0109] To overcome the bottleneck of insufficient correlation between the dynamic degradation of the membrane material anti-fouling layer and the evolution of the carbon footprint during the transportation phase in the carbon emission accounting of seawater desalination systems, and to further improve the accuracy of cross-stage data coupling and adaptability to extreme environments, an innovative approach based on spatiotemporal dynamic coupling is proposed to address the defects of the traditional method of static transportation carbon emission coefficient and separation of membrane performance degradation from transportation history. In some embodiments, the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient during the transportation phase in the carbon footprint factor set is calculated based on the operating time data of the seawater desalination device, and the additional carbon emission correction value for membrane performance degradation is generated, including:

[0110] 301. Extract the distribution of extreme values ​​of ambient temperature and the frequency of water inlet pressure fluctuations from the operating time data of the seawater desalination device, and combine them with the initial performance parameters of the anti-fouling layer in the factory inspection report of the membrane module to generate a set of dynamic influencing factors for the degradation rate of the membrane material anti-fouling layer;

[0111] In step 301 , the extreme value distribution of the ambient temperature refers to statistical distribution characteristic data consisting of the maximum and minimum values ​​of the ambient temperature collected during the operation of the seawater desalination device.

[0112] In this embodiment of the present application, a fuzzy C-means clustering algorithm is used to perform multimodal distribution analysis on the ambient temperature extremes (e.g., a daily maximum temperature of 45°C ± 3°C) in the operating time data, generating a temperature sensitivity level (high / medium / low). The dominant frequency component (0.1-5Hz) of the inlet pressure fluctuation is extracted through wavelet transform. Combined with the initial thickness (e.g., 120μm) and porosity (0.25μm) of the anti-fouling layer detected at the factory of the membrane module, a degradation rate prediction model based on decision tree regression is constructed. Input parameters include the proportion of temperature extreme duration (28% of the time period is >40°C), the peak frequency of pressure fluctuation (2.3Hz), and initial performance parameters, and a set of dynamic influencing factors is output.

[0113] 302. Based on the spatiotemporal distribution characteristics of the carbon emission coefficient during the transportation phase in the carbon footprint factor set, and by taking into account the correlation between the ambient temperature and humidity gradients along the transportation route and the hygroscopic expansion rate of the membrane material, a dynamic attenuation model of the carbon emission coefficient during the transportation phase and the initial performance parameters of the anti-pollution layer is established;

[0114] In step 302, the dynamic attenuation model characterizes the mathematical relationship between the carbon emission coefficient during the transportation phase and the attenuation of the initial performance parameters of the anti-pollution layer of the membrane material as the environment changes.

[0115] In this application example, a kriging interpolation method was used to construct a spatial distribution surface of ambient temperature and humidity (1 km × 1 km resolution) based on GPS trajectory data from the transportation route. The Pearson correlation coefficient (r = 0.76) between the hygroscopic expansion rate of the membrane material (0.02%-0.15% per hour) and the temperature and humidity gradient was extracted. A multidimensional regression model was constructed using a support vector machine. The input parameters included the average humidity during the transportation phase (82% ± 7%), the extreme diurnal temperature difference (12°C), and the initial anti-pollution layer thickness. The output was a dynamic attenuation model. A Markov Chain Monte Carlo (MCMC) method was used to perform Bayesian posterior estimation of the model parameters.

[0116] 303. Introducing the nonlinear amplification coefficient of the membrane material crystal defect density on the degradation rate of the membrane material anti-pollution layer under a high temperature and high salt environment, combined with the set of dynamic influencing factors, calculate the spatiotemporal coupling degree between the degradation rate of the membrane material anti-pollution layer and the carbon emission coefficient of the transportation stage;

[0117] In step 303, the nonlinear amplification factor is a nonlinear enhancement factor reflecting the effect of the membrane material's crystal defect density on the degradation rate of the anti-fouling layer in a high-temperature, high-salt environment. The spatiotemporal coupling degree is an indicator that quantifies the synergistic effect between the degradation rate of the anti-fouling layer and the carbon emission coefficient during the transportation phase in the spatiotemporal dimensions.

[0118] In this example, scanning electron microscopy (SEM) was used to obtain the density distribution of crystalline defects in the membrane material (0-15 defects / μm²), and the spatial complexity of the defects was characterized by fractal dimension calculation (D=1.82±0.03). A radial basis function neural network (RBFNN)-based amplification factor prediction model was constructed, with input parameters including the operating environment salt concentration (38,000-45,000 ppm), temperature gradient (ΔT=8°C / h), and defect density. A firefly optimization algorithm (FA) was used to determine the spatiotemporal coupling between the degradation rate and the transport carbon emission coefficient. The fitness function included transport distance (a 25% increase in weight for defects greater than 3,000 km) and a defect density threshold (a nonlinear 1.8-fold increase in coupling for defects greater than 10 defects / μm²).

[0119] 304. Based on the spatiotemporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane assembly, an additional carbon emission correction value for membrane performance degradation is generated by multiplying the degradation rate of the anti-pollution layer of the membrane material and the attenuation of the carbon emission coefficient in the transportation stage.

[0120] In step 304 , the salt concentration and temperature gradient data refer to composite parameters consisting of the salt concentration change rate (ppm / h) and the temperature spatial difference (° C. / m) in the actual operating environment of the membrane module.

[0121] In this example, a multi-physics coupled simulation was used to calculate the effect of a salt concentration gradient (ΔC = 2000 ppm / m) on the electrochemical potential of the membrane surface (-35 mV to -58 mV). Combined with temperature gradient data (ΔT = 2.3°C / m) collected by an infrared thermal imager, a 16×16 matrix of environmental correction factors for spatiotemporal coupling was generated. A quantum genetic algorithm (QGA) was used to optimize the weighted product of the degradation rate (e.g., 0.026 μm / h) and the transport carbon emission attenuation (e.g., 0.019 kg CO₂e / km), subject to constraints such as the membrane material's Young's modulus (2.4 GPa) and operating voltage stability (fluctuation <5%). The resulting output was an additional carbon emission correction value for membrane performance degradation.

[0122] Here's a specific example:

[0123] A SWRO plant in the Yanbu Industrial Zone of Saudi Arabia uses PROC10 membrane modules (with an initial anti-fouling layer thickness of 115 μm and a transport distance of 3,850 km). Based on step 301, an analysis of ambient temperature extremes revealed a daily average high temperature of 46°C for 6.2 hours, with a dominant frequency of inlet water pressure fluctuations of 2.8 Hz. The decision tree model outputted a high temperature factor of 1.45 and a pressure factor of 0.93. In step 302, the temperature and humidity interpolation surface for the transport route indicated a maximum humidity of 91%. The support vector machine model calculated a transport carbon emission coefficient attenuation rate of 5.3%, with the dynamic attenuation coefficient matrix reaching a value of 0.78 for the northeast region. In step 303, SEM analysis revealed a crystal defect density of 12 per μm². The RBFNN model outputted a nonlinear amplification factor of 1.65, and the firefly algorithm calculated a spatiotemporal coupling of 1.72. Step 304 yielded a salt concentration gradient of 2500 ppm / m² and a temperature gradient of 2.8°C / m². Quantum genetic optimization generated an additional carbon emission correction value of 1.8 kg CO₂e / h due to membrane performance degradation.

[0124] Steps 301-304 construct a cross-stage, multi-physics field coupled dynamic correction system for seawater desalination carbon emissions through cluster analysis of ambient temperature extremes, spatial interpolation modeling of transport temperature and humidity, fractal quantification of crystal defects, and quantum optimization algorithms. Under complex working conditions such as long-distance transportation and high temperature and high salinity, the correlation accuracy between the degradation rate of the anti-pollution layer and transport carbon emissions is significantly improved, effectively solving industry problems such as the lack of transport environmental memory effect and misjudgment of accelerated degradation caused by defects in traditional methods. By integrating historical transport data with real-time operating parameters, environmentally adaptive generation of carbon emission correction values ​​for membrane performance degradation is achieved, providing an industrial-grade carbon accounting solution with millimeter-level spatial resolution and hourly timeliness for highly volatile seawater desalination scenarios, greatly improving the integrity of carbon emission tracking throughout the life cycle and the reliability of the compensation transaction triggering mechanism.

[0125] To overcome the bottlenecks of data fragmentation and insufficient adaptability to the service environment in the construction of the carbon footprint factor library for seawater desalination systems, an innovative approach of full-lifecycle data integration and dynamic correction is proposed to address the problems of static carbon factor libraries and disconnected environmental parameters. In some embodiments, the carbon footprint factor set is formed by integrating carbon emission-related data from the entire life cycle of seawater desalination membrane materials. The carbon dioxide equivalent value per unit area in the production stage is matched to the corresponding carbon emission quantification relationship based on the membrane module type, including:

[0126] 401. Obtain original membrane material production data, extract multi-dimensional carbon emission parameters including membrane material sintering temperature curve and chemical solvent consumption, and divide the multi-dimensional carbon emission parameters into discrete carbon footprint units based on the porosity distribution characteristics and anti-pollution layer thickness parameters corresponding to the membrane component type;

[0127] In step 401, the multi-dimensional carbon emission parameters include peak / constant temperature data for the membrane material sintering temperature curve and production-phase carbon emission parameters associated with unit chemical solvent consumption. Discrete carbon footprint units are the basic units for carbon emission calculation, divided according to the membrane module porosity distribution and anti-fouling layer thickness.

[0128] In the embodiment of the present application, principal component analysis (PCA) is used to extract features from the sintering temperature curve, and principal components (such as peak temperature and heating slope) with a variance contribution rate of >85% are selected. The residual chemical solvent is analyzed by near-infrared spectroscopy, and a carbon footprint unit partitioning model based on K-means clustering is constructed in combination with the porosity laser scanning data of the membrane module (accuracy 0.1μm). The input parameters include the porosity standard deviation (0.02-0.35μm), the anti-pollution layer thickness gradient (50-150μm) and the solvent consumption intensity (L / m²). The output is a discretized carbon footprint unit matrix (dimension 32×32), and each discretized carbon footprint unit corresponds to the carbon emission baseline value for a specific porosity range.

[0129] 402. For the production stage data in the discretized carbon footprint unit, establish a dynamic mapping rule between the membrane module type and the carbon dioxide equivalent value per unit area. The dynamic mapping rule corrects the carbon emission deviation between different membrane module batches by coupling the crystallinity gradient of the membrane material with the sintering energy consumption.

[0130] In step 402, the dynamic mapping rule establishes a nonlinear relationship between membrane module type and carbon emissions per unit area, dynamically adjusting the relationship based on crystallinity gradient and sintering energy consumption. The carbon emissions deviation represents the difference in carbon emissions between different production batches due to process fluctuations.

[0131] In this example, X-ray diffraction (XRD) was used to obtain membrane material crystallinity gradient data (full width at half maximum 0.15°-0.45°). A Gaussian mixture model (GMM) was used to construct a probability distribution surface for the membrane material crystallinity gradient and sintering energy consumption (kWh / m²). A Bayesian network was used to infer the optimal mapping relationship for different membrane module types (e.g., roll-to-roll / flat-sheet). Input nodes included parameters such as sintering furnace thermal efficiency (65-82%) and inert gas consumption (0.5-1.2 L / m²). A particle swarm optimization (PSO) algorithm was used to correct for batch-to-batch variations, with a constraint of a carbon emission difference threshold (<5%) between adjacent batches. This ultimately generated a dynamic mapping rule.

[0132] 403. Integrate the ambient temperature and humidity sensor data during the membrane material transportation phase and the measured degradation rate data during the disposal phase, expand the discretized carbon footprint unit into a continuous carbon footprint factor covering the production, transportation, and disposal phases, and generate an independent data index identifier for the continuous carbon footprint factor based on the membrane module serial number and the dynamic mapping rule;

[0133] In step 403, the continuous carbon footprint factor is a spatiotemporal continuous carbon emission quantification parameter formed by integrating the data of the three stages of production, transportation, and disposal. The data index identifier is a unique data location code generated based on the membrane module serial number and dynamic mapping rules.

[0134] In this embodiment, a LoRa wireless sensor network was deployed to collect temperature and humidity data during the transportation phase (with a sampling interval of 5 minutes). Combined with degradation rate data from seawater immersion experiments during the disposal phase (annual average mass loss rate of 0-2%), a three-stage data fusion model was constructed using a temporal convolutional network (TCN). A hierarchical clustering algorithm (HCA) was used to expand the discretized carbon footprint units into continuous factors, increasing the feature dimension to 64. An improved hash algorithm was used to generate independent data index identifiers, including the membrane module serial number (16 bits), production date (Unix timestamp), and dynamic mapping version number (v1.2.3), enabling rapid retrieval of 100,000 data items per second.

[0135] 404. Based on the historical distribution data of salinity and temperature of the service environment of the seawater desalination membrane material and in combination with the data index identifier, the continuous carbon footprint factor is corrected for service adaptability to generate a carbon footprint factor set including the physical property parameters of the membrane material and the environmental adaptability coefficient.

[0136] In step 404, the service adaptability correction is to adjust the dynamic weight of the carbon footprint factor based on the salinity-temperature historical data of the actual operating environment. The environmental adaptability coefficient is a dynamic adjustment parameter that reflects the impact of a specific salinity-temperature combination on the carbon footprint factor.

[0137] In the embodiment of the present application, a variational autoencoder (VAE) model is constructed to learn the joint distribution characteristics of historical data of salinity (30,000-50,000 ppm) and temperature (20-50°C), and the potential space dimension is set to 8 dimensions. The environmental adaptation coefficient is calculated by the random forest (RF) algorithm, and the input features include the frequency of salinity mutation (times / month), the amplitude of daily average temperature fluctuation (ΔT=5-15°C), and the thermal expansion coefficient of the membrane material (1.2-2.8×10⁻ 5 The environmental adaptability coefficient and the continuous carbon footprint factor are integrated by using tensor splicing technology to perform service adaptability correction, and finally a carbon footprint factor set containing 128 physical property parameters is generated.

[0138] Here's a specific example:

[0139] Assume that a TM820D-400 membrane module (porosity 0.22μm±0.03, anti-fouling layer thickness 135μm) is used in the Jebel Ali desalination plant in the United Arab Emirates. In step 401, principal component analysis is performed on the sintering temperature curve, extracting the peak temperature of 382°C and the constant temperature time of 2.8h. Combined with the solvent consumption of 0.8L / m², K-means clustering is used to generate 16 discrete carbon footprint units. In step 402, the XRD detection shows that the half-peak width of the crystallinity is 0.28°. The GMM model shows that the optimal sintering energy consumption range is 18-22kWh / m². PSO correction The carbon emission difference between batches was reduced to 3.7%; in step 403, the humidity was monitored to be >90% for 120 hours during the transportation phase, and the TCN model integrated the waste degradation data of 0.9% / year to generate an independent data index identifier "TM820D-2023-047#V1.3"; in step 404, the VAE model analyzed the salinity fluctuation range of the plant to be 38000-43000ppm and the daily average ΔT=12℃, and the random forest calculated the environmental adaptability coefficient to be 1.15. The final carbon footprint factor set included 128 parameters such as the thermal conductivity of the membrane material and the ion adsorption rate.

[0140] Steps 401-404 utilize principal component feature extraction, Gaussian mixture modeling, time-series convolution fusion, and variational autoencoder optimization to construct the world's first environmentally adaptive carbon footprint factor library for the entire life cycle of membrane materials. This significantly improves carbon accounting accuracy and cross-stage data collaboration capabilities in complex operating environments, effectively addressing core flaws of traditional methods such as the disconnect between production and transportation data and delayed response to in-service environmental conditions. This provides a comprehensive carbon management solution for desalination systems, from nanoscale material properties to kilometer-scale transportation routes, enabling dynamic and precise tracking of carbon footprint factors and real-time environmental adaptation.

[0141] To overcome the technical bottlenecks of insufficient spatial resolution and poor dynamic correlation of pollutant deposition in membrane fouling monitoring in seawater desalination systems, an innovative approach based on graphene multimodal sensing and three-dimensional thermal modeling is proposed to address the shortcomings of traditional methods, such as single-point monitoring blind spots and linearized estimation of contamination thickness, and to achieve holographic monitoring of membrane surface fouling status and precise backwash control. In some embodiments, a graphene sensor array is fixed on the membrane surface of the reverse osmosis membrane assembly to collect the instantaneous rate of change of membrane flux and the fluctuation value of salt retention efficiency in real time. The amount of pollutant deposition on the membrane surface is determined based on the fluctuation value of salt retention efficiency, including:

[0142] 501. Arrange a graphene sensor array in a honeycomb topology within a predetermined area on the active layer surface of a reverse osmosis membrane assembly, adjust the spacing density of the graphene sensor array according to the flow channel distribution characteristics of the membrane assembly, and physically connect electrode leads of the graphene sensor array to an embedded signal acquisition module on an end cap of the membrane assembly;

[0143] In step 501, the honeycomb topology refers to an array of graphene sensing elements arranged in a regular hexagonal geometric pattern on the membrane surface, achieving optimal spatial coverage density and signal anti-interference properties. The embedded signal acquisition module is a high-speed signal processing unit integrated into the membrane module end cap, enabling real-time conversion and preprocessing of sensor data.

[0144] In this embodiment, computational fluid dynamics (CFD) was used to simulate the vortex distribution characteristics within the reverse osmosis membrane flow channel (Reynolds number Re = 1200-2500), identifying regions with high velocity gradients (velocity difference > 0.8 m / s) as key monitoring locations for the sensor array. A graphene field-effect transistor array was fabricated on the surface of a polyamide active layer using photolithography. The cell size was 50 μm × 50 μm, and the pitch density was dynamically adjusted to 300-600 μm based on the flow channel width (0.5-2 mm). The 256-channel electrode leads were connected to the embedded FPGA-based signal acquisition module within the end cap using gold wire ball bonding. Signal transmission delay was controlled to < 2 μs, and synchronous triggering accuracy reached ±5 ns.

[0145] 502. Based on the adjusted spacing density, the instantaneous change rate of the membrane flux and the fluctuation value of the salt rejection efficiency are synchronously captured by the periodic scanning mode of the graphene sensor array, and the current signal and the ion adsorption characteristic spectrum of the membrane surface micro-area are synchronously collected in a single scanning cycle, and the high-frequency noise in the signal is filtered out and the valid data associated with the characteristic frequency band of membrane fouling is retained;

[0146] In step 502, the periodic scanning mode is a trigger mechanism that cyclically collects data from each sensor unit at a fixed time interval (eg, 10 ms). The membrane fouling characteristic frequency band is a specific signal frequency range that is strongly associated with the contaminant deposition process.

[0147] In this embodiment of the present application, a time-frequency joint analysis system is constructed for periodic scanning, and a wavelet packet decomposition algorithm is used to divide the original signal (sampling rate 1MHz) into 32 sub-bands. A convolutional neural network (CNN) is used to identify intensity changes in the Cl⁻ ion adsorption characteristic spectrum (Raman shift 258cm⁻¹±3cm⁻¹). The input layer contains time-domain waveforms, frequency-domain energy, and spatial position encoding. An IIR digital filter bank is designed with a cutoff frequency set to 100Hz (stopband attenuation >60dB) to retain low-frequency components associated with pollutant deposition. Valid data segments are extracted through adaptive threshold segmentation, and the signal-to-noise ratio (SNR) is increased to above 32dB.

[0148] 503. Constructing a dynamic correlation model of membrane surface pollutant deposition based on the temporal variation trend of the salt rejection efficiency fluctuation value, and converting the attenuation slope of the salt rejection efficiency fluctuation value into a pollutant deposition thickness distribution value in combination with the membrane module surface roughness parameter and the operating pressure gradient data;

[0149] In step 503 , the temporal variation trend is a curve shape characteristic formed by the fluctuation value of the salt retention efficiency over time.

[0150] In this example, fractal geometry theory was used to analyze the surface roughness parameters of the membrane module (fractal dimension D = 2.15-2.35), and a particle swarm optimization algorithm based on the Lévy flight modification (LFPSO) was constructed to solve the deposition thickness distribution. Input parameters included the operating pressure gradient (0.2-0.8 MPa / m), the decay slope (0.5-3% / h), and the surface contact angle (55-75°). The contaminant deposition stress distribution (0-15 kPa) was calculated through finite element multiphysics coupling. Combined with the contaminant component percentages (e.g., 65% CaCO3 and 22% SiO2) calibrated by X-ray photoelectron spectroscopy (XPS), the contaminant deposition thickness distribution value was output.

[0151] 504. Based on the spatial heterogeneity distribution of the instantaneous change rate of the membrane flux and the distribution value of the pollutant deposition thickness, a three-dimensional thermal map of the pollutant accumulation hotspot area is constructed on the flow channel cross section of the membrane surface, and the backwash cycle optimization parameters of the current operation stage are associated to determine the pollutant deposition amount on the membrane surface.

[0152] In step 504, the spatial heterogeneity distribution is the non-uniform difference in the instantaneous rate of membrane flux in different areas of the surface. The backwash cycle optimization parameters are the backwash trigger time, pressure gradient and other control parameters that are dynamically adjusted according to the degree of pollutant accumulation.

[0153] In the embodiment of this application, a spatial correlation model between flux and contamination thickness was constructed based on the Kriging interpolation method, generating a 500×500 pixel three-dimensional heat map (color scale resolution 0.1μm). The backwash strategy was optimized using a reinforcement learning (DQN) algorithm. The state space included the proportion of hotspot area (>15% warning), thickness gradient (Δh>2μm / mm), and energy consumption constraints (<5kWh / m³). A dynamic verification system based on digital twins was designed, and the optimized parameters were sent to the PLC controller in real time. The pulse backwash pressure was set to 8-12MPa, and the duration was optimized to 18-25 seconds to determine the amount of contaminant deposition on the membrane surface.

[0154] Here's a specific example:

[0155] Assume that an Israeli desalination plant uses Dow SW30HRLE-400 membrane modules to implement this solution. In step 501, CFD simulations identify the central region of the flow channel (1.2 mm width) as a high vortex zone. 256 graphene sensor units are deployed (with a spacing density adjusted to 450 μm) and connected to an embedded signal acquisition module to achieve 128-channel parallel acquisition. In step 502, when the inlet salinity is 40,000 ppm, the Raman peak intensity decreases by 18%. Wavelet packet decomposition is used to extract the 32-64 Hz characteristic frequency band, increasing the signal-to-noise ratio (SNR) to 35 dB. In step 503, the LFPSO algorithm calculates the deposition thickness in the northwest quadrant to be 5.3 μm (contact angle 68°), and XPS analysis shows that CaCO3 accounts for 72%. In step 504, thermodynamic diagrams identify three hotspots (accounting for 19% of the total area). The DQN model optimizes the backwash cycle to every 4.2 hours, with a pressure pulse setting of 10 MPa / 22 seconds. The flux recovery rate reaches 98.5%, and the amount of contaminant deposition on the membrane surface is determined.

[0156] Steps 501-504, through fluid dynamics-guided sensor array deployment, time-frequency combined signal analysis, fractal geometry optimization modeling, and digital twin strategy verification, have established the first millimeter-level holographic pollution status monitoring system in the desalination industry. Under complex influent conditions, this system achieves submicron spatial resolution and minute-by-minute dynamic updates of pollutant deposition thickness, significantly improving the accuracy and timeliness of backwash strategies. This effectively addresses core issues in traditional solutions, such as the lack of blind spot monitoring and large errors in linear thickness estimation. This system provides a new technological paradigm for intelligent maintenance and energy efficiency optimization of reverse osmosis membranes.

[0157] To address the technical bottlenecks of inaccurate regional credit matching and delayed dynamic environmental response in carbon offset transactions for desalination systems, an intelligent compensation mechanism based on blockchain spatial indexing and renewable energy forecasting is proposed. This mechanism breaks through the limitations of static quota allocation and manual review delays in traditional offset transactions, achieving second-level response and precise compensation for carbon emission violations. In some embodiments, when the real-time carbon emission increment exceeds a preset emission increment threshold, the smart contract in the blockchain network automatically matches the renewable energy credits tied to the geographical location of the desalination device, and generates and executes a targeted carbon offset transaction request based on the renewable energy credits, including:

[0158] 601. Preset emission increment thresholds and geographic location matching rules in the blockchain network to establish a grid-based binding relationship between the geographic location of the desalination plant and the database of renewable energy credit suppliers in the region;

[0159] In step 601, the gridded binding relationship is a data structure that divides the geographic space into standard grid cells, establishing a fixed regional association between the desalination plant location and the renewable energy credit supplier. The emission increment threshold is the critical value of carbon emissions exceeding the standard that triggers carbon offset transactions, and is dynamically set based on the plant type and environmental capacity.

[0160] In this embodiment, the Geohash algorithm is used to encode geographic coordinates (with latitude and longitude accuracy of 0.001°) into 12-bit strings (e.g., "sf3y9z"), establishing a standard 500m×500m grid system. Renewable energy credit pool data (wind power / photovoltaic quotas and historical transaction records) for each grid is stored in the PostGIS spatial database, and an R-tree index is constructed to accelerate geographic range queries. An on-chain smart contract is deployed to pre-set threshold logic. A bilinear interpolation algorithm is used to dynamically calculate threshold curves based on the installed capacity (e.g., 10,000 m³ / d) and environmental sensitivity factors (e.g., a coefficient of 1.2 for mangrove conservation areas). This ultimately generates a grid-based binding relationship (primary key: grid code, foreign key: credit provider ID).

[0161] 602. Receive the operating status data of the seawater desalination device in real time, extract the timestamp and geographic coordinate information of the real-time carbon emission increment, and verify the spatial consistency of the geographic coordinate information and the grid binding relationship;

[0162] In step 602, the spatial consistency verification is a process of confirming the matching of the grid to which the real-time geographic coordinates of the seawater desalination device belong and the preset binding relationship.

[0163] In this embodiment, a streaming data processing pipeline is designed to receive device sensor data in real time via Apache Kafka (with a sampling interval of 1 second). A Flink window function is used to extract the average of the last 5 minutes of real-time carbon emission increments. Spatial inclusion is checked using the JTS topology library. After converting the device's GPS coordinates (e.g., longitude 46.2°E, latitude 24.5°N) into Geohash encoding, a prefix match (at least the first 8 digits) is performed against the grid encoding stored on the blockchain. Verification failure triggers an exception handling process (such as retransmission or manual review), generating a spatial consistency proof using a zero-knowledge proof (zk-STARK) and writing it to the event log.

[0164] 603. When the real-time carbon emission increment exceeds the emission increment threshold and the verification results are inconsistent, searching the renewable energy credit pool according to the grid number corresponding to the geographic coordinate information, and dynamically adjusting the tradable credit weight in combination with the current regional wind power forecast data and photovoltaic irradiance monitoring data;

[0165] In step 603, the tradable credit weight is a priority coefficient reflecting the tradable amount of different renewable energy types under current time and space conditions.

[0166] In this embodiment, the meteorological bureau's API is accessed to obtain wind power forecast data (wind speed and direction) and photovoltaic irradiance monitoring values ​​(W / m²). A random forest regression model is used to calculate credit weights. Input features include the 1-hour wind power output forecast error (RMSE), photovoltaic array efficiency degradation rate (daily average 0.5-1.2%), and grid absorption capacity (MW). A back-propagation neural network (BPNN) is used to dynamically adjust the weight distribution of tradable credits, subject to constraints such as the remaining credit pool (e.g., wind power quota ≥ 30%) and a transaction cost threshold (≤ 0.12 USD / kWh).

[0167] 604. Generate a targeted carbon compensation transaction request including the incremental carbon emission compensation value, the timestamp, and the grid number according to the tradable credit weight, complete the credit transfer through the atomic exchange protocol of the blockchain smart contract, and write the transaction execution result into the cross-chain distributed ledger.

[0168] In step 604, the atomic swap protocol is an indivisibility protocol that ensures that blockchain transactions are either completely successful or completely rolled back. The cross-chain distributed ledger is a distributed storage structure that supports data interoperability between different blockchain networks.

[0169] In this embodiment, an atomic swap process based on a hashed time-locked contract (HTLC) is designed to generate a structured request containing the compensation value (e.g., 5.2MWh), the timestamp (ISO 8601 format), and a trellis code. Transaction data is synchronized to the Energy Trading Consortium (ETC) and Carbon Emission Registry (CER) chains via Polkadot cross-chain bridging technology, and a joint evidence index is constructed using a Merkle-Patricia tree. The transaction results are stored in IPFS shards (shard size 256KB), generating a CID hash as the evidence fingerprint. Finally, the credit limit transfer is completed in a Hyperledger Fabric channel, with transaction latency controlled within 3 seconds.

[0170] Here's a specific example:

[0171] Assume that a SWRO unit (12,000 m³ / d) in the Yanbu Industrial Zone in Saudi Arabia triggers the compensation process at 2:30 PM on October 5, 2023. Step 601 indicates that the unit is located at the Geohash grid "sf3y9z" (center point 46.18°E, 24.48°N), with a preset wind power quota of 50 MWh and photovoltaic power quota of 80 MWh. The threshold is set at 3.2 kg CO₂e / h. Step 602 indicates that the real-time carbon emission increment is 3.8 kg. CO2e / h, extract the coordinates 46.1823°E, 24.4795°N, and verify that the Geohash match matches the first 9 digits "sf3y9z1" successfully. Step 603 predicts a 12% decrease in wind speed and an increase in photovoltaic irradiance to 920W / m² over the next hour. Random forest weights are calculated (0.6 for wind power and 0.9 for photovoltaic power), dynamically adjusting the tradable credits to 32MWh for wind power and 72MWh for photovoltaic power. Step 604 generates a request {compensation amount: 4.3kg CO2e / h, timestamp: 2023-10-05T14:30:00Z, grid: sf3y9z}, completing the transfer of 3.5MWh of photovoltaic credits via HTLC. The cross-chain evidence CID is "bafy...q2vm."

[0172] Steps 601-604, through geocoded grid binding, streaming spatial validation, weather-driven weight optimization, and atomic cross-chain transactions, establish a carbon offset system with precise matching and instantaneous delivery of renewable energy credits. This significantly improves the dynamic adaptability and transaction reliability of credit allocation under complex weather conditions, effectively addressing core issues such as regional matching bias and rigid credit weighting in traditional solutions. This provides a full-process blockchain solution for desalination systems, from millimeter-level coordinate positioning to megawatt-level credit transfers, enabling second-level response and trusted closed-loop management of carbon emission violations.

[0173] To address the significant batch-to-batch deviations caused by process fluctuations in the carbon emission quantification model during the membrane module production phase and the lack of adaptability of traditional static mapping rules, a carbon footprint correction mechanism based on the dynamic coupling of sintering process and crystallinity is proposed. This overcomes the limitations of the linear extrapolation model in traditional methods and achieves dynamic and accurate adaptation of membrane module carbon emission quantification. In some embodiments, the dynamic mapping rules for establishing the membrane module type and the carbon dioxide equivalent value per unit area are established. The dynamic mapping rules correct the carbon emission deviations between different membrane module batches by coupling the gradient of the membrane material crystallinity change with the sintering energy consumption, including:

[0174] 701. Extract historical sintering process data of membrane module production batches, combine with the measured value of the membrane material crystallinity gradient, and generate a correlation between the membrane material crystallinity gradient and the sintering process parameters;

[0175] In step 701, the historical sintering process data is a time series record of parameters such as the sintering temperature curve, holding time, and inert gas flow rate during the membrane module production process. The crystallinity gradient is a characteristic of the rate at which the crystallinity of the membrane material changes with temperature during the sintering process.

[0176] In the embodiments of the present application, X-ray diffraction (XRD) technology is used to obtain the crystallinity gradient data of the membrane material, and the half-peak width change gradient is calculated by Lorentz fitting. Principal component analysis (PCA) is performed on the sintering temperature curve to extract key characteristic parameters (heating rate, peak temperature duration, cooling slope). A Gaussian process regression model is constructed, and the input dimensions include peak temperature (350-420°C), pressure fluctuation in the holding stage (±0.5MPa) and argon flow rate (10-25L / min), and the correlation surface between the crystallinity gradient and the process parameters is output. The consistency of the data distribution is verified by the Kolmogorov-Smirnov test, and finally 32 sets of correlation relationships between the crystallinity change gradient of the membrane material and the sintering process parameters are generated.

[0177] 702. Divide the energy consumption distribution in the historical sintering process data into a high energy consumption interval and a low energy consumption interval based on the association relationship, the porosity distribution characteristics, and the anti-pollution layer thickness parameter, and establish a segmented mapping relationship between the membrane module type and the carbon dioxide equivalent value per unit area;

[0178] In step 702, the segmented mapping relationship is a nonlinear correspondence rule between the membrane module types divided according to different energy consumption intervals and the carbon emission values ​​per unit area.

[0179] In the present embodiment, a fuzzy C-means clustering algorithm (FCM) was used to perform multimodal segmentation on historical energy consumption data, with a membership threshold of 0.7 set to determine interval boundaries. Based on the laser scanning data of the membrane assembly porosity (0.1-0.4μm) and the anti-pollution layer thickness gradient (50-200μm), a quantile regression model was constructed to establish a segmented mapping relationship. A radial basis function neural network (RBFNN) was used to fit the nonlinear relationship in the high energy consumption range, and a multivariate linear regression (MLR) was used in the low energy consumption range. The model inputs included parameters such as the sintering furnace thermal efficiency (65-82%) and the membrane layer density (1.2-1.8g / cm³).

[0180] 703. Calculate a carbon emission correction factor based on actual sintering energy consumption data of the membrane assembly production batch and the coupling relationship between the crystallinity gradient of the membrane material and the sintering energy consumption;

[0181] In step 703, the coupling relationship is a quantitative indicator reflecting the interaction strength between the crystallinity gradient and the sintering energy consumption. The carbon emission correction coefficient is a dynamic correction factor used to adjust the carbon emission baseline value per unit area.

[0182] In this example, a partial least squares regression (PLSR) model was constructed to calculate the coupling strength based on actual sintering energy consumption data (kWh / m²) and crystallinity gradient (% / °C). Monte Carlo simulation was used to generate 10,000 sets of process parameter combinations, and Sobol index analysis was used to determine the key influencing factors (peak temperature sensitivity accounted for 58%). An adaptive particle swarm optimization (APSO) algorithm was designed to solve for the optimal carbon emission correction factor, constrained by a threshold for carbon emission differences between adjacent batches (<5%). The carbon emission correction factor output range was set to 0.8-1.2, with a step size of 0.01.

[0183] 704. Bind the carbon emission correction coefficient to the production serial code of the membrane module production batch, update the carbon emission quantification relationship in the segmented mapping relationship, and correct the carbon emission deviation between different membrane module batches.

[0184] In step 704, the production serial code is a code that uniquely identifies the production batch of the membrane module, including the manufacturer code, production date, and production line number. The carbon emission quantitative relationship is a set of corresponding rules between the modified membrane module type and the carbon emission value per unit area.

[0185] In this embodiment, a blockchain-based mapping relationship update protocol is designed. The carbon emission correction factor is bound to the production serial number (e.g., "DOW-20231005-L3") and then written into a smart contract. Historical mapping versions are stored using a Merkle tree structure, with each node containing a hash value, a timestamp, and a version number (v1.2.3). The compliance of the correction operation is verified using zero-knowledge proofs (zk-SNARKs). The updated carbon emission quantification relationship is stored in an IPFS distributed storage (shard size 128KB) and synchronized to all authenticated nodes. A version rollback mechanism ensures traceability of carbon emission deviation corrections, supporting backward compatibility with three historical versions.

[0186] Here's a specific example:

[0187] This solution was implemented for a Saudi Arabian desalination plant's October 2023 production batch (serial code "SWC5-2310-B7"). In step 701, XRD detected a crystallinity gradient of 0.28% / °C. PCA extracted key sintering parameters (peak temperature 395°C, 2.6h holding time), generating a correlation R32 between the membrane material's crystallinity gradient and the sintering process parameters. In step 702, FCM was used to divide energy consumption into intervals (high energy consumption > 27.3 kWh / m²), and RBFNN was used to establish a segmented mapping relationship (a porosity of 0.22 μm corresponds to a carbon emission of 1.35 kg CO₂e / m²). In step 703, APSO was used to calculate a carbon emission correction factor of 1.08 (actual energy consumption 28.1 kWh / m², crystallinity gradient 0.31% / °C), adjusting the carbon emission value to 1.46 kg CO₂e / m²). CO2e / m²; Step 704 binds the carbon emission correction factor to the serial code, updates the Ethereum smart contract mapping table, and stores the updated carbon emission quantification relationship in IPFS as "bafy...q2vm". Historical versions are retained up to v1.2.2.

[0188] Steps 701-704 establish a dynamic correction system for carbon emissions from membrane module production through multimodal process data analysis, dynamic interval mapping modeling, coupled optimization calculations, and blockchain-based evidence storage and traceability. This significantly improves the consistency of carbon emissions quantification between batches in complex process fluctuation scenarios, effectively addressing the baseline deviation problem caused by traditional static models. This provides a process-parameter-driven dynamic correction paradigm for carbon management throughout the reverse osmosis membrane lifecycle, enabling precise carbon footprint tracking from nanoscale material properties to the production batch level.

[0189] To overcome the technical bottlenecks of fuzzy decomposition phase demarcation and insufficient spatial resolution in the dynamic monitoring of membrane fouling in seawater desalination systems, this solution proposes a pollutant deposition quantification method based on the linkage between salt retention efficiency fluctuations and pressure gradients. This method addresses the problems of delayed response to sudden changes in pollutant deposition rates and linearized thickness estimation by traditional monitoring methods, thereby achieving millimeter-level dynamic analysis of the pollutant deposition process. In some embodiments, a dynamic correlation model for membrane surface pollutant deposition is constructed based on the time-domain variation trend of the salt retention efficiency fluctuation value. Combined with the membrane module surface roughness parameters and operating pressure gradient data, the attenuation slope of the salt retention efficiency fluctuation value is converted into a pollutant deposition thickness distribution value, including:

[0190] 801. Extracting the time-domain variation data of the salt rejection efficiency fluctuation value, obtaining the initial variation rate and the stable variation rate of the attenuation slope, and dividing the rapid accumulation stage and the slow accumulation stage of pollutant deposition into the combination of the membrane module surface roughness parameter;

[0191] In step 801, the time domain variation data is a continuous variation curve data formed by the fluctuation value of the salt interception efficiency over time. The rapid accumulation stage is an accelerated deposition period in which the pollutant deposition rate exceeds a set threshold.

[0192] In the examples of this application, a wavelet transform was used to perform multi-scale decomposition (scaling factors 0.1-10) of the salt retention efficiency fluctuation curve to extract the transient variation characteristics of the attenuation slope. The initial rate of change (0-5 minutes) and the stable rate of change (>30 minutes) were calculated using the first-order derivative. Combined with the membrane surface roughness parameters (Ra = 0.1-0.8μm) obtained by atomic force microscopy (AFM), a stage division model based on fractal theory (fractal dimension D = 1.6-2.3) was constructed. A dual-threshold detection algorithm was designed: when the rate change gradient is >0.5% / min and the roughness increment is >0.05μm / h, it is determined to be a rapid accumulation phase; the rest is a slow accumulation phase.

[0193] 802. Divide the membrane assembly surface into high-pressure areas and low-pressure areas according to the spatial distribution characteristics of the operating pressure gradient data, and establish regional mapping relationships between the attenuation slope and the pollutant deposition thickness;

[0194] In step 802, the spatial distribution characteristics of the pressure gradient data are the spatial variation patterns formed by the operating pressure differences in different regions of the membrane assembly surface. The regional mapping relationship is the quantitative correspondence rule between the attenuation slope and the deposition thickness in different pressure regions.

[0195] In the examples of this application, the pressure field distribution on the reverse osmosis membrane surface (pressure range 0.5-8 MPa) was simulated based on computational fluid dynamics (CFD), and the Kriging interpolation method (Kriging) was used to generate a pressure gradient contour map (resolution 50 μm). The membrane surface was divided into a high-pressure core area (pressure > 5 MPa) and a low-pressure edge area (pressure < 3 MPa) through k-means clustering. The high-pressure area was fitted with an exponential kernel function for nonlinear relationships, while the low-pressure area was fitted with a linear kernel function. The input parameters included the attenuation slope (0.2-3% / h), contact angle (55-75°), and flow rate (0.5-2 m / s), and the output was a regional mapping relationship between the attenuation slope and the pollutant deposition thickness.

[0196] 803. Based on the influence of the membrane assembly surface roughness parameter on the attenuation slope, as well as the rapid accumulation stage and the slow accumulation stage, the calculated deposition thickness in the regional mapping relationship is corrected to generate an initial distribution of pollutant deposition thickness;

[0197] In step 803 , the initial distribution of pollutant deposition thickness is a basic distribution model of deposition thickness that does not consider dynamic pressure changes.

[0198] In the examples of this application, a scanning electron microscope (SEM) was used to obtain three-dimensional surface morphology data of the membrane (scanning step size 0.1 μm), and the influence coefficient of roughness on contaminant adhesion was quantified (0.8-1.5) by power spectral density analysis (PSD). A correction model based on a convolutional neural network (CNN) was constructed to correct the calculated deposition thickness in the regional mapping relationship. The input layer includes a pressure partition map (512×512 pixels), an initial thickness distribution, and a roughness heat map (Ra value encoding). A transfer learning method was used to fine-tune the network parameters (learning rate 0.001) based on the ImageNet pre-trained model, and output the corrected initial distribution of contaminant deposition thickness (accuracy ±0.3 μm).

[0199] 804. Combine the time domain variation trend of the attenuation slope with the dynamic variation of the operating pressure gradient data, update the initial distribution of pollutant deposition thickness, and convert the attenuation slope into a pollutant deposition thickness distribution value.

[0200] In step 804, the dynamic changes are combined to perform spatiotemporal coupling analysis on the decay rate trend that changes with time and the spatial pressure gradient evolution.

[0201] In this embodiment, a spatiotemporal fusion algorithm was designed, employing a long short-term memory (LSTM) network to capture the temporal trend of the attenuation slope (with a 60-minute window), combined with real-time updated pressure gradient data (sampling rate 1Hz). Tensor concatenation was used to fuse the temporal features (16 dimensions) with the spatial features (64 dimensions) into an 80-dimensional feature vector. The optimal thickness distribution was determined using a quantum particle swarm optimization (QPSO) algorithm, with a fitness function that incorporates a thickness gradient constraint (Δh < 1 μm / mm) and an energy consumption limit (backwash cycle > 4 hours). This ultimately generates a contaminant deposition thickness distribution with a 95% confidence interval.

[0202] Here's a specific example:

[0203] Consider a Fujairah desalination plant in the United Arab Emirates, which uses Toray UTC-80 membrane modules for operational monitoring. Step 801 detects an 18% drop in salt retention efficiency in the first hour. Wavelet analysis extracts an initial rate of 1.2% / min, and the roughness Ra increases from 0.3 μm to 0.5 μm, indicating a rapid accumulation phase for the first 45 minutes. In step 802, CFD simulations reveal a high-pressure zone (6.2 MPa) in the northwest quadrant. An exponential model (R²=0.92) is established for regional mapping, predicting a deposition thickness of 5.8 μm in this area. In step 803, SEM scans reveal a surface pit density of 32 per μm². The CNN model, after correction, adjusts the calculated deposition thickness to 6.3 μm (with a correction factor of 1.08). In step 804, the LSTM predicts a decrease in the decay slope to 0.4% / min over the next hour. QPSO optimization generates a distribution of contaminant deposition thickness, triggering an increase in backwash pressure to 9 MPa / 25 seconds, restoring flux to 97%.

[0204] Steps 801-804 utilize multi-scale time-domain analysis, spatial pressure field modeling, microscopic topography correction, and quantum optimization algorithms to construct a dynamic monitoring and precise prediction system for membrane fouling deposition thickness. Under complex operating conditions, this significantly improves the accuracy of contaminant deposition stage classification and the spatial resolution of thickness calculations. This effectively addresses core issues inherent in traditional methods, such as time-domain response lag and neglect of pressure distribution. This provides comprehensive technical support for intelligent maintenance of reverse osmosis membranes, from capturing changes in the second level to analyzing thickness at the millimeter level, significantly enhancing system operational stability and energy efficiency management.

[0205] To address the issues of missing physical and digital mapping and inefficient cross-chain verification in the storage of carbon offset transaction data in desalination systems, a storage management mechanism based on encrypted embedded identification and multi-chain collaboration is proposed. This mechanism overcomes the limitations of traditional storage methods, such as data silos and high tampering risks, and achieves the immutability of carbon offset transaction data and a strong binding between it and physical devices. In some embodiments, the directional carbon offset transaction request is encrypted and stored in a blockchain distributed ledger, forming a full-chain storage covering membrane material production traceability, operational carbon emissions tracking, and offset transaction verification. The unique identification code of the full-chain storage is written into the membrane component identification chip, including:

[0206] 901. Encrypt the targeted carbon offset transaction request to generate an encrypted transaction data packet embedded with the membrane module identity code;

[0207] In step 901, the membrane module identity code is embedded in the data structure of the encrypted transaction data packet by inserting the unique serial number of the membrane module as metadata. The encrypted transaction data packet is a standardized data unit that contains the carbon compensation amount and timestamp sensitive information and is protected by a cryptographic algorithm.

[0208] In this example, the SM4 algorithm, a nationally recognized encryption algorithm, is used to symmetric encrypt the targeted carbon offset transaction request (with a 256-bit key length). CBC (Cipher Block Chaining) mode is selected for encryption, and an initialization vector (IV) is generated. Transaction data is encapsulated in JSON-LD format, with the module identity code (16-bit ASCII code) embedded in the metadata layer. The SHA-3 hash algorithm is used to generate a data packet integrity check code. This ultimately generates an encrypted transaction data packet (Header+Payload+MAC) that complies with the ISO / IEC 7816 standard. The data packet size is compressed to less than 512 bytes to ensure efficient blockchain writing.

[0209] 902. Writing the encrypted transaction data packet into the blockchain distributed ledger, establishing a chain association in the ledger for membrane material production traceability, operation carbon emission tracking, and compensation transaction verification, thereby forming a full chain of evidence storage;

[0210] In step 902, the chained association establishes an irreversible, sequential relationship between production, operation, and compensation data in the blockchain ledger. The full chained evidence is a complete chain of blockchain-verified data evidence covering the entire life cycle of the film material.

[0211] In this embodiment, a distributed ledger architecture based on Hyperledger Fabric is designed to create three types of smart contracts within a channel: a production traceability contract (storing sintering process hash values), an operation tracking contract (recording carbon emission time series), and a compensation verification contract (storing transaction vouchers). A Merkle-Patricia tree is used to construct a data association index, breaking down encrypted data packets into 128-byte blocks and writing them into different contracts. Cross-contract calls enable chained data association, forming a fully chained evidence storage system.

[0212] 903. Perform distributed verification on the full chain of evidence based on the blockchain consensus mechanism to generate a unique identification code for the evidence including the transaction hash value;

[0213] In step 903, distributed verification is the process by which multiple blockchain nodes perform consistency checks on the stored evidence data based on a consensus algorithm. The unique identification code for the stored evidence is a non-repeatable verification code generated from parameters such as the transaction hash value and the blockchain height.

[0214] In this embodiment, a modified PBFT (Practical Byzantine Fault Tolerance) consensus mechanism is deployed, with four validating nodes (orderers) and 12 endorsing nodes (peers). The distributed verification process includes broadcasting the hash value of the encrypted data packet between nodes; verifying data integrity through zero-knowledge proofs (zk-STARKs); and executing smart contract condition checks (e.g., ensuring the compensation amount does not exceed a preset threshold). Once consensus is reached, the Keccak-256 algorithm is used to generate a unique identification code for the stored evidence, "0x8b3d...a9c4_5894321_TM820D-047" (format: TXID_BlockHeight_MembraneID).

[0215] 904. Write the unique identification code of the evidence into the membrane component identification chip through the physical interface, and record the evidence writing timestamp and blockchain node verification information.

[0216] In step 904, the physical interface write is the physical process of burning the digital evidence information into the physical chip through the hardware interface. The node verification information includes the blockchain node ID participating in the consensus verification, the timestamp, and the audit data.

[0217] In an embodiment of the present application, a femtosecond laser micromachining system (wavelength 1030nm, pulse energy 1.2mJ) is used to ablate a two-dimensional code matrix (20×20 dot matrix, depth 2μm) on the surface of the membrane component identification chip (silicon nitride substrate). The membrane component identification chip is written through a physical interface, and the written data includes a certificate identification code (ASCII encoding), a Unix timestamp (millisecond level) and a verification node ID list (Base64 encoding). Near-field communication (NFC) reading and verification are achieved through the ISO / IEC 14443 protocol, and the reading distance is controlled within 2cm to ensure data security. The chip storage area is divided into a secure isolation area, and private key access must be authenticated by a physical anti-disassembly mechanism.

[0218] Here's a specific example:

[0219] Assuming that Qatar's Ras Laffan desalination plant completes carbon offset trading in October 2023, in step 901, the compensation request (5.2MWh of PV credits) is encrypted using SM4-CBC with the key "9f3a...c7b1" and the membrane module ID "UTC80-2310-B5" embedded to generate a 512-byte encrypted transaction data packet. In step 902, the data packet is split into a production hash (CID: bafk...q2v), an operation trace (timestamp 1696523400), and a compensation voucher (transaction hash 0x9e8a...f3c2) in the Fabric channel, which are then written into three smart contracts to form a fully chained evidence store. In step 903, the four orderer nodes obtained reach consensus through PBFT and generate a unique identification code for the evidence store, "0x9e8a...f3c2_5894321_UTC80-B5". In step 904, the Trumpf TruMicro The 5000 series laser burns a QR code on the chip, storing the blockchain node verification information list "Node1-4:2023-10-05T14:30:00.123Z". During third-party audits, NFC reading is used to verify the full chain evidence.

[0220] Steps 901-904 establish a trusted evidence storage system for the entire lifecycle of carbon offset trading data through national-level encryption encapsulation, multi-smart contract collaborative evidence storage, improved consensus verification, and laser micromachining physical anchoring. In complex industrial environments, this system irreversibly binds digital evidence to physical devices, effectively addressing core issues in traditional solutions such as data tampering and the lack of physical-digital mapping. This provides a comprehensive evidence storage solution for desalination systems, from bit-level data security to nanometer-level physical recording.

[0221] Figure 2 The present invention provides a block chain-based seawater desalination system carbon emission accounting and management system structure diagram, such as Figure 2 As shown, the system includes:

[0222] Matching module 21, for integrating carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a carbon footprint factor set, in which the carbon dioxide equivalent value per unit area during the production phase is matched to the corresponding carbon emission quantification relationship based on the membrane module type;

[0223] Acquisition module 22, for fixing the graphene sensor array on the membrane surface of the reverse osmosis membrane assembly, collecting the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and determining the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value;

[0224] a calculation module 23 for dynamically calculating the real-time incremental carbon emissions due to membrane performance degradation based on the instantaneous rate of change of the membrane flux, the amount of pollutant deposition on the membrane surface, and the set of carbon footprint factors, combined with the operating time of the desalination device and the influent salt concentration data;

[0225] A generation module 24 is configured to automatically match renewable energy credits associated with the geographical location of the desalination plant through a smart contract in the blockchain network when the real-time carbon emission increment exceeds a preset emission increment threshold, and to generate and execute a targeted carbon offset transaction request based on the renewable energy credits;

[0226] The storage module 25 is used to encrypt and store the directed carbon compensation transaction request in the blockchain distributed ledger, forming a full-chain evidence covering membrane material production traceability, operation carbon emission tracking and compensation transaction verification, and write the unique identification code of the full-chain evidence into the membrane component identification chip.

[0227] Figure 2 The blockchain-based seawater desalination system carbon emission accounting and management system can execute Figure 1 The implementation principles and technical effects of the blockchain-based seawater desalination system carbon emission accounting and management method described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the blockchain-based seawater desalination system carbon emission accounting and management system described in the above embodiment has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0228] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A blockchain-based carbon emission accounting and management method for a seawater desalination system, characterized in that: include: Integrate carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a carbon footprint factor set, in which the carbon dioxide equivalent value per unit area in the production stage is matched with the corresponding carbon emission quantification relationship according to the membrane component type; A graphene sensor array is fixed on the membrane surface of the reverse osmosis membrane module to collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and the amount of pollutant deposition on the membrane surface is determined based on the salt retention efficiency fluctuation value; Dynamically calculate the real-time carbon emission increment due to membrane performance degradation based on the instantaneous rate of change of membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, combined with the operating time of the seawater desalination device and the influent salt concentration data; When the real-time carbon emissions increase exceeds a preset emission increase threshold, the smart contract in the blockchain network automatically matches the renewable energy credits tied to the geographical location of the desalination plant, and generates and executes a targeted carbon offset transaction request based on the renewable energy credits; Encrypting and storing the targeted carbon offset transaction request in a blockchain distributed ledger to form a full-chain evidence system covering membrane material production traceability, operational carbon emissions tracking, and offset transaction verification, and writing the unique identification code of the full-chain evidence system into the membrane module identification chip; The method dynamically calculates the real-time carbon emission increment due to membrane performance degradation based on the instantaneous change rate of the membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, in combination with the operating time of the seawater desalination device and the influent salt concentration data, including: Extracting the spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining it with the carbon emission coefficient of the membrane material degradation stage in the carbon footprint factor set, and generating a dynamic correlation baseline between the flux decay rate and carbon emissions, wherein the dynamic correlation baseline is represented by a weight matrix, and the weight matrix includes the weight ratio of the flux decay rate and carbon emissions; Establishing a nonlinear response relationship between the influent salt concentration data and the instantaneous change rate of the membrane flux, correcting the spatial heterogeneity of the flux decay rate by the amount of pollutant deposition on the membrane surface, and correcting the weight distribution of the dynamic correlation baseline; Based on the operating time data of the seawater desalination device, the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage in the carbon footprint factor set is calculated to generate an additional carbon emission correction value for membrane performance degradation; The real-time carbon emission increment due to membrane performance degradation is dynamically calculated by integrating the corrected weight distribution and the additional carbon emission correction value due to membrane performance degradation, combined with the dynamic impact factor of the influent salt concentration data on the membrane surface charge density; The method of calculating the coupling degree between the degradation rate of the anti-fouling layer of the membrane material and the carbon emission coefficient of the transportation stage in the carbon footprint factor set based on the operating time data of the seawater desalination device, and generating the additional carbon emission correction value for membrane performance degradation, includes: Extract the distribution of ambient temperature extremes and the frequency of water inlet pressure fluctuations from the operating time data of the seawater desalination device. Combined with the initial performance parameters of the anti-fouling layer in the factory inspection report of the membrane module, a set of dynamic influencing factors on the degradation rate of the membrane material anti-fouling layer is generated. Based on the spatiotemporal distribution characteristics of the carbon emission coefficient during the transportation phase in the carbon footprint factor set, and by the correlation between the ambient temperature and humidity gradient in the transportation route and the hygroscopic expansion rate of the membrane material, a dynamic attenuation model of the carbon emission coefficient during the transportation phase and the initial performance parameters of the anti-pollution layer is established; The nonlinear amplification coefficient of the membrane material crystal defect density on the degradation rate of the membrane material anti-fouling layer under high temperature and high salt environment is introduced, and combined with the dynamic influencing factor set, the spatiotemporal coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage is calculated; Based on the spatiotemporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane assembly, the additional carbon emission correction value for membrane performance degradation is generated by multiplying the degradation rate of the membrane material anti-fouling layer by the attenuation of the carbon emission coefficient in the transportation stage.

2. The method according to claim 1, characterized in that The carbon footprint factor set is formed by integrating the carbon emission related data of the whole life cycle of the seawater desalination membrane material, wherein the carbon dioxide equivalent value per unit area in the production stage is matched with the corresponding carbon emission quantitative relationship according to the membrane module type, including: Obtaining raw membrane material production data, extracting multi-dimensional carbon emission parameters such as the membrane material sintering temperature curve and chemical solvent consumption, and dividing the multi-dimensional carbon emission parameters into discrete carbon footprint units based on the porosity distribution characteristics and anti-pollution layer thickness parameters corresponding to the membrane component type; For the production stage data in the discretized carbon footprint unit, a dynamic mapping rule is established between the membrane module type and the carbon dioxide equivalent value per unit area. The dynamic mapping rule corrects the carbon emission deviation between different membrane module batches by coupling the crystallinity gradient of the membrane material with the sintering energy consumption; By integrating the ambient temperature and humidity sensor data during the transportation phase of the membrane material and the measured degradation rate data during the disposal phase, the discretized carbon footprint unit is expanded into a continuous carbon footprint factor covering the production, transportation, and disposal phases. The continuous carbon footprint factor generates an independent data index identifier based on the membrane module serial number and the dynamic mapping rule; Based on the historical distribution data of salinity and temperature in the service environment of the seawater desalination membrane material, combined with the data index identifier, the continuous carbon footprint factor is corrected for service adaptability to generate a carbon footprint factor set including the physical property parameters of the membrane material and the environmental adaptability coefficient. The service adaptability correction refers to adjusting the dynamic weight of the carbon footprint factor according to the historical distribution data of salinity and temperature in the service environment.

3. The method according to claim 1, characterized in that The method includes fixing a graphene sensor array on the membrane surface of the reverse osmosis membrane assembly, collecting the instantaneous change rate of the membrane flux and the salt retention efficiency fluctuation value in real time, and determining the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value, including: Arranging a graphene sensor array in a honeycomb topology within a preset area on the active layer surface of a reverse osmosis membrane assembly, adjusting the spacing density of the graphene sensor array according to the flow channel distribution characteristics of the membrane assembly, and physically connecting the electrode leads of the graphene sensor array to the embedded signal acquisition module of the membrane assembly end cap; Based on the adjusted spacing density, the graphene sensor array is used in a periodic scanning mode to synchronously capture the instantaneous change rate of membrane flux and the fluctuation value of salt retention efficiency, and the current signal and ion adsorption characteristic spectrum of the membrane surface microarea are synchronously collected in a single scanning cycle, and high-frequency noise in the signal is filtered out while retaining valid data associated with the characteristic frequency band of membrane fouling; A dynamic correlation model of membrane surface pollutant deposition is constructed based on the temporal variation trend of the salt rejection efficiency fluctuation value, and the attenuation slope of the salt rejection efficiency fluctuation value is converted into a pollutant deposition thickness distribution value in combination with the membrane module surface roughness parameter and the operating pressure gradient data; Based on the spatial heterogeneity distribution of the instantaneous change rate of the membrane flux and the distribution value of the pollutant deposition thickness, a three-dimensional thermal map of the pollutant accumulation hotspot area is constructed on the flow channel cross-section of the membrane surface, and the backwash cycle optimization parameters of the current operation stage are associated to determine the pollutant deposition amount on the membrane surface.

4. The method according to claim 1, wherein When the real-time carbon emission increment exceeds a preset emission increment threshold, the smart contract in the blockchain network automatically matches the renewable energy credits bound to the geographical location of the desalination device, and generates and executes a targeted carbon compensation transaction request based on the renewable energy credits, including: Preset emission increment thresholds and geographic location matching rules in the blockchain network to establish a grid-based binding relationship between the geographic location of the desalination plant and the renewable energy credit supplier database in the region; receiving the operating status data of the seawater desalination device in real time, extracting the timestamp and geographic coordinate information of the real-time carbon emission increment, and verifying the spatial consistency of the geographic coordinate information and the grid binding relationship; When the real-time carbon emission increment exceeds the emission increment threshold and the verification results are inconsistent, the renewable energy credit pool is retrieved according to the grid number corresponding to the geographic coordinate information, and the tradable credit weight is dynamically adjusted in combination with the current regional wind power forecast data and photovoltaic irradiance monitoring data; A targeted carbon compensation transaction request including the incremental carbon emission compensation value, the timestamp, and the grid number is generated according to the tradable credit weight, the credit transfer is completed through the atomic exchange protocol of the blockchain smart contract, and the transaction execution result is written into the cross-chain distributed ledger.

5. The method according to claim 2, characterized in that The dynamic mapping rule between the membrane module type and the carbon dioxide equivalent value per unit area is established. The dynamic mapping rule corrects the carbon emission deviation between different membrane module batches by coupling the relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption, including: Extracting historical sintering process data of membrane module production batches, combining it with the measured value of the membrane material crystallinity gradient, and generating a correlation between the membrane material crystallinity gradient and the sintering process parameters; According to the correlation relationship, the porosity distribution characteristics and the anti-pollution layer thickness parameter, the energy consumption distribution in the historical sintering process data is divided into a high energy consumption interval and a low energy consumption interval, and a segmented mapping relationship between the membrane module type and the carbon dioxide equivalent value per unit area is established respectively; Based on the actual sintering energy consumption data of the membrane module production batch, the carbon emission correction factor is calculated through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption; The carbon emission correction coefficient is bound to the production serial code of the membrane module production batch, the carbon emission quantification relationship in the segmented mapping relationship is updated, and the carbon emission deviation between different membrane module batches is corrected.

6. The method according to claim 3, characterized in that The method of constructing a dynamic correlation model of membrane surface pollutant deposition based on the time domain variation trend of the salt rejection efficiency fluctuation value, combining the membrane module surface roughness parameter and the operating pressure gradient data, and converting the attenuation slope of the salt rejection efficiency fluctuation value into a pollutant deposition thickness distribution value includes: Extracting the time domain variation data of the salt rejection efficiency fluctuation value, obtaining the initial change rate and the stable change rate of the attenuation slope, and combining the surface roughness parameters of the membrane module to divide the rapid accumulation stage and the slow accumulation stage of pollutant deposition; According to the spatial distribution characteristics of the operating pressure gradient data, the membrane assembly surface is divided into high-pressure areas and low-pressure areas, and regional mapping relationships between the attenuation slope and the pollutant deposition thickness are established respectively; Based on the influence of the membrane assembly surface roughness parameter on the attenuation slope, as well as the rapid accumulation stage and the slow accumulation stage, the calculated deposition thickness value in the regional mapping relationship is corrected to generate an initial distribution of pollutant deposition thickness; The time domain variation trend of the attenuation slope is combined with the dynamic variation of the operating pressure gradient data to update the initial distribution of pollutant deposition thickness, and the attenuation slope is converted into a pollutant deposition thickness distribution value.

7. The method according to claim 1, characterized in that The encrypted storage of the targeted carbon offset transaction request in the blockchain distributed ledger forms a full-chain evidence storage covering membrane material production traceability, operation carbon emission tracking, and offset transaction verification, and the unique identification code of the full-chain evidence storage is written into the membrane module identification chip, including: Encrypting the targeted carbon offset transaction request to generate an encrypted transaction data packet embedded with the membrane module identity code; Writing the encrypted transaction data packet into the blockchain distributed ledger, establishing a chain association in the ledger for membrane material production traceability, operation carbon emission tracking, and compensation transaction verification, forming a full chain of evidence; Perform distributed verification of the full-chain evidence based on the blockchain consensus mechanism to generate a unique identification code for the evidence containing the transaction hash value; The unique identification code of the evidence is written into the membrane component identification chip through the physical interface, and the evidence writing timestamp and blockchain node verification information are recorded.

8. A blockchain-based seawater desalination system carbon emission accounting and management system, characterized by: include: A matching module is used to integrate carbon emission data related to the entire life cycle of seawater desalination membrane materials to form a set of carbon footprint factors, in which the carbon dioxide equivalent value per unit area during the production phase is matched to the corresponding carbon emission quantification relationship based on the membrane module type; An acquisition module is used to fix a graphene sensor array on the membrane surface of the reverse osmosis membrane assembly, collect the instantaneous change rate of membrane flux and the salt retention efficiency fluctuation value in real time, and determine the amount of pollutant deposition on the membrane surface based on the salt retention efficiency fluctuation value; a calculation module for dynamically calculating the real-time incremental carbon emissions due to membrane performance degradation based on the instantaneous rate of change of the membrane flux, the amount of pollutant deposition on the membrane surface, and the set of carbon footprint factors, in combination with the operating time of the desalination device and the influent salt concentration data; A generation module is configured to automatically match renewable energy credits tied to the geographical location of the desalination device through a smart contract in the blockchain network when the real-time carbon emission increment exceeds a preset emission increment threshold, and generate and execute a targeted carbon offset transaction request based on the renewable energy credits; A storage module is used to encrypt and store the targeted carbon offset transaction request in a blockchain distributed ledger, forming a full-chain evidence storage covering membrane material production traceability, operation carbon emission tracking, and offset transaction verification, and write the unique identification code of the full-chain evidence storage into the membrane module identification chip; The method dynamically calculates the real-time carbon emission increment due to membrane performance degradation based on the instantaneous change rate of the membrane flux, the amount of pollutant deposition on the membrane surface, and the carbon footprint factor set, in combination with the operating time of the seawater desalination device and the influent salt concentration data, including: Extracting the spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining it with the carbon emission coefficient of the membrane material degradation stage in the carbon footprint factor set, and generating a dynamic correlation baseline between the flux decay rate and carbon emissions, wherein the dynamic correlation baseline is represented by a weight matrix, and the weight matrix includes the weight ratio of the flux decay rate and carbon emissions; Establishing a nonlinear response relationship between the influent salt concentration data and the instantaneous change rate of the membrane flux, correcting the spatial heterogeneity of the flux decay rate by the amount of pollutant deposition on the membrane surface, and correcting the weight distribution of the dynamic correlation baseline; Based on the operating time data of the seawater desalination device, the coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage in the carbon footprint factor set is calculated to generate an additional carbon emission correction value for membrane performance degradation; The real-time carbon emission increment due to membrane performance degradation is dynamically calculated by integrating the corrected weight distribution and the additional carbon emission correction value due to membrane performance degradation, combined with the dynamic impact factor of the influent salt concentration data on the membrane surface charge density; The method of calculating the coupling degree between the degradation rate of the anti-fouling layer of the membrane material and the carbon emission coefficient of the transportation stage in the carbon footprint factor set based on the operating time data of the seawater desalination device, and generating the additional carbon emission correction value for membrane performance degradation, includes: Extract the distribution of ambient temperature extremes and the frequency of water inlet pressure fluctuations from the operating time data of the seawater desalination device. Combined with the initial performance parameters of the anti-fouling layer in the factory inspection report of the membrane module, a set of dynamic influencing factors on the degradation rate of the membrane material anti-fouling layer is generated. Based on the spatiotemporal distribution characteristics of the carbon emission coefficient during the transportation phase in the carbon footprint factor set, and by the correlation between the ambient temperature and humidity gradient in the transportation route and the hygroscopic expansion rate of the membrane material, a dynamic attenuation model of the carbon emission coefficient during the transportation phase and the initial performance parameters of the anti-pollution layer is established; The nonlinear amplification coefficient of the membrane material crystal defect density on the degradation rate of the membrane material anti-fouling layer under high temperature and high salt environment is introduced, and combined with the dynamic influencing factor set, the spatiotemporal coupling degree between the degradation rate of the membrane material anti-fouling layer and the carbon emission coefficient of the transportation stage is calculated; Based on the spatiotemporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane assembly, the additional carbon emission correction value for membrane performance degradation is generated by multiplying the degradation rate of the membrane material anti-fouling layer by the attenuation of the carbon emission coefficient in the transportation stage.

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