Block chain-based seawater desalination system carbon emission accounting management method and system

Abstract

The invention provides a block chain-based seawater desalination system carbon emission accounting management method and system. Wherein a carbon footprint factor library is constructed by integrating full-life-cycle carbon emission data of a seawater desalination membrane material, and a carbon emission chemical relationship is associated in combination with a membrane type in a production stage; a graphene sensor array is used for monitoring the reverse osmosis membrane flux instantaneous change rate and the salt interception efficiency fluctuation value in real time, and the pollutant deposition amount is calculated; dynamically calculating the real-time carbon emission increment in the operation process based on the membrane performance degradation data and the carbon footprint factor; when the increment exceeds a threshold value, automatically matching and binding a green telecom quota of a geographic position through a block chain smart contract, and triggering a directional carbon compensation transaction; and finally, the transaction information is encrypted and stored in a block chain account book to form a full-chain evidence covering production traceability, operation tracking and compensation verification, and the full-chain evidence is written into a membrane module chip to realize closed-loop management of carbon emission. According to the technical scheme provided by the invention, the accuracy of carbon emission accounting of the seawater desalination system is remarkably improved.

Description

Technical Field

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

[0002] In the carbon emission management of seawater desalination systems, there is an urgent need for an automated closed-loop management solution covering production, operation, and compensation. In particular, it is necessary to solve the dynamic coupling problem between the carbon emission factor of membrane material production and the operation loss data to avoid accounting deviations due to data faults. In addition, existing technologies lack effective monitoring methods for carbon emission fluctuations caused by real-time pollutant deposition, and the compensation mechanism is difficult to achieve accurate matching of green electricity resources bound to the geographical location of the equipment.

[0003] In the existing technology, carbon emission monitoring solutions based on IoT sensors and life cycle static databases are widely used. This solution deploys pressure and temperature sensors to collect membrane component operating parameters, combines the preset membrane material production carbon emission static database, and establishes a linear growth model to predict carbon emission trends. When the transmembrane pressure difference or energy consumption exceeds a fixed threshold, the regional green telecom credit quota is matched through a centralized trading platform for carbon compensation, and the compensation quota is uniformly converted according to the grid carbon emission factor.

[0004] However, this solution has significant defects. 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 carbon emission increments. Second, the carbon compensation trigger mechanism relies on a fixed energy consumption threshold, which cannot capture the sudden change in carbon emissions caused by the rapid deposition of pollutants, and is prone to compensation lag or over-compensation. Finally, Green Telecom's credit quota matching relies on the geographic grid data of a centralized platform, lacks a device-level geographic location binding verification mechanism, and the compensation transaction records are stored in a centralized database, which poses a risk of data tampering. Summary of the invention

[0005] The present 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 prior art.

[0006] In the first aspect, the present application provides a blockchain-based seawater desalination system carbon emission accounting management method, including:

[0007] Integrate the carbon emission-related data of the whole 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 in the production stage is matched with the corresponding carbon emission quantitative relationship according to the membrane component type;

[0008] A graphene sensor array is fixed on the membrane surface of the reverse osmosis membrane assembly to collect the instantaneous change rate of the 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] According to the instantaneous change rate of the membrane flux, the amount of pollutant deposition on the membrane surface and the carbon footprint factor set, combined with the operation time of the seawater desalination device and the inlet salt concentration data, the real-time carbon emission increment caused by the degradation of membrane performance is dynamically calculated;

[0010] When the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credits bound to the geographical location of the desalination device are automatically matched through the smart contract in the blockchain network, and a targeted carbon compensation transaction request is generated and executed 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 real-time carbon emission increment caused by membrane performance degradation is dynamically calculated 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, combined with the operation time of the seawater desalination device and the inlet salt concentration data, including:

[0013] Extracting the spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining 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 anti-fouling layer of the membrane material 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, calculating the coupling degree between the degradation rate of the anti-fouling layer of the membrane material and the carbon emission coefficient in the transportation stage in the carbon footprint factor set based on the operation duration data of the seawater desalination device, and generating an additional carbon emission correction value for membrane performance deterioration, including:

[0018] Extracting the extreme environmental temperature distribution and the inlet water pressure fluctuation frequency in the operation duration data of the seawater desalination device, and combining 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 influence factors for the degradation rate of the anti-fouling layer of the membrane material;

[0019] Based on the spatio-temporal distribution characteristics of the carbon emission coefficient in the transportation stage in the carbon footprint factor set, establishing a dynamic attenuation model between the carbon emission coefficient in the transportation stage and the initial performance parameters of the anti-fouling layer through the correlation degree between the environmental temperature and humidity gradient in the transportation route and the moisture absorption expansion rate of the membrane material;

[0020] Introducing a non-linear amplification coefficient of the crystal defect density of the membrane material in a high-temperature and high-salt environment to the degradation rate of the anti-fouling layer of the membrane material, and combining with the set of dynamic influence factors to calculate the spatio-temporal coupling degree between the degradation rate of the anti-fouling layer of the membrane material and the carbon emission coefficient in the transportation stage;

[0021] Based on the spatio-temporal coupling degree and the salt concentration and temperature gradient data in the actual operation environment of the membrane module, generating an additional carbon emission correction value for membrane performance deterioration through the product relationship between the degradation rate of the anti-fouling layer of the membrane material and the attenuation amount of the carbon emission coefficient in the transportation stage.

[0022] Optionally, integrating the carbon emission correlation data of the entire life cycle of the seawater desalination membrane material to form a carbon footprint factor set, where the carbon dioxide equivalent value per unit area in the production stage matches the corresponding carbon emission quantification relationship according to the type of membrane module, including:

[0023] Obtaining the original production data of the membrane material, extracting multi-dimensional carbon emission parameters of the sintering temperature curve and chemical solvent consumption of the membrane material, and dividing the multi-dimensional carbon emission parameters into discrete carbon footprint units according to the porosity distribution characteristics and anti-fouling layer thickness parameters corresponding to the type of membrane module;

[0024] For the production stage data in the discrete carbon footprint units, establishing a dynamic mapping rule between the type of membrane module and the carbon dioxide equivalent value per unit area, and the dynamic mapping rule corrects the carbon emission deviation amount between different batches of membrane modules through the coupling relationship between the change gradient of the crystallinity of the membrane material and the sintering energy consumption;

[0025] By integrating the environmental temperature and humidity sensor data of the membrane material transportation stage and the measured degradation rate data of the disposal stage, the discretized carbon footprint unit is expanded into a continuous carbon footprint factor including the production, transportation and disposal stages. The continuous carbon footprint factor generates an independent data index identifier according to the membrane component serial number and the dynamic mapping rule;

[0026] Based on the historical distribution data of salinity and temperature of 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 membrane material physical property parameters and environmental adaptability coefficients.

[0027] Optionally, the graphene sensor array is fixed 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 the amount of pollutant deposition on the membrane surface is determined based on the salt retention efficiency fluctuation value, including:

[0028] Arrange a graphene sensor array in a honeycomb topological structure in a preset 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 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 instantaneous change rate of membrane flux and the fluctuation value of salt retention efficiency are synchronously captured through the periodic scanning mode of the graphene sensor array, and the micro-area current signal and ion adsorption characteristic spectrum of the membrane surface 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 pollution is retained;

[0030] Based on the time-domain variation trend of the salt retention efficiency fluctuation value, a dynamic correlation model of pollutant deposition on the membrane surface is constructed, and the attenuation slope of the salt retention efficiency fluctuation value is converted into a pollutant deposition thickness distribution value in combination with the membrane component surface roughness parameter and the operating pressure gradient data;

[0031] According to 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 renewable energy credits bound to the geographical location of the desalination device are automatically matched through a smart contract in the blockchain network, and a targeted carbon compensation transaction request is generated and executed according to the renewable energy credits, including:

[0033] Preset an emission increment threshold and a geographical location matching rule in the blockchain network, and establish a grid binding relationship between the geographical location of the seawater desalination device and the database of renewable energy credit suppliers in the region where it is located;

[0034] Receive the operation status data of the seawater desalination device in real time, extract the timestamp and geographical coordinate information of the real-time carbon emission increment, and verify the spatial consistency between the geographical coordinate information and the grid binding relationship;

[0035] When the real-time carbon emission increment exceeds the emission increment threshold and the verification result is inconsistent, retrieve the renewable energy credit pool according to the grid number corresponding to the geographical coordinate information, and dynamically adjust the tradable credit weight in combination with the current regional wind power generation power prediction data and photovoltaic irradiance monitoring data;

[0036] Generate a directional carbon compensation trading request including the carbon emission increment compensation value, the timestamp and the grid number according to the tradable credit weight, complete the credit transfer through the atomic swap protocol of the blockchain smart contract, and write the transaction execution result into the cross-chain distributed ledger.

[0037] Optionally, establish the dynamic mapping rule between the membrane component type and the carbon dioxide equivalent value per unit area. The dynamic mapping rule corrects the carbon emission deviation between different batches of membrane components through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption, including:

[0038] Extract the historical sintering process data of the membrane component production batch, and generate the correlation relationship between the crystallinity change gradient of the membrane material and the sintering process parameters in combination with the measured value of the crystallinity change gradient of the membrane material;

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

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

[0041] Bind the carbon emission correction coefficient to the production sequence code of the membrane component production batch, update the carbon emission quantification relationship in the segmented mapping relationship, and correct the carbon emission deviation between different batches of membrane components.

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

[0043] Extract the time-domain change data of the salt rejection efficiency fluctuation value, obtain the initial change rate and the stable change rate of the attenuation slope, and combine 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 surface of the membrane module is divided into a high-pressure area and a low-pressure area, and the regional mapping relationship between the attenuation slope and the pollutant deposition thickness is established respectively;

[0045] Based on the influence of the surface roughness parameters of the membrane module on the attenuation slope, and the rapid accumulation stage and the slow accumulation stage, correct the calculated deposition thickness value in the regional mapping relationship to generate the initial distribution of the pollutant deposition thickness;

[0046] Combine the time-domain change trend of the attenuation slope with the dynamic change of the operating pressure gradient data to update the initial distribution of the pollutant deposition thickness, and convert the attenuation slope into a pollutant deposition thickness distribution value.

[0047] Optionally, encrypting and storing the directional carbon compensation trading request in the blockchain distributed ledger to form a full-chain evidence storage covering membrane material production traceability, operating carbon emission tracking and compensation trading verification, and writing the unique identification code of the full-chain evidence storage into the membrane module identity recognition chip, including:

[0048] Encrypt the directional carbon compensation trading request to generate an encrypted trading data packet embedded with the membrane module identity code;

[0049] Write the encrypted trading data packet into the blockchain distributed ledger, establish a chain association of membrane material production traceability, operating carbon emission tracking and compensation trading verification in the ledger to form a full-chain evidence storage;

[0050] Based on the blockchain consensus mechanism, conduct distributed verification on the full-chain evidence storage to generate a unique identification code of the evidence storage including the transaction hash value;

[0051] Write the unique identification code of the evidence storage into the membrane module identity recognition chip through a physical interface, and record the evidence storage writing timestamp and the blockchain node verification information.

[0052] In a second aspect, the present application provides a carbon emission accounting management system for a seawater desalination system based on blockchain, including:

[0053] A matching module is used to integrate the carbon emission-related data of the whole 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 in the production stage is matched with the corresponding carbon emission quantitative relationship according to the membrane component type;

[0054] A collection 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 the 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 carbon emission increment caused by 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, combined with the operation time of the seawater desalination device and the inlet salt concentration data;

[0056] A generation module, which is used to automatically match the renewable energy credits bound 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 compensation 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 to form 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.

[0058] In the embodiment of the present application, the carbon emission associated data of the whole life cycle of the seawater desalination membrane material is integrated to form a carbon footprint factor set, wherein the carbon dioxide equivalent value per unit area in the production stage matches 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 component to collect the instantaneous change rate of the 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; according to the instantaneous change rate of the membrane flux, the amount of pollutant deposition on the membrane surface and the carbon footprint factor set, combined with the operation time of the seawater desalination device and the influent salt concentration data, the real-time carbon emission increment caused by the degradation of membrane performance is dynamically calculated; when the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credit bound to the geographical location of the seawater desalination device is automatically matched through the smart contract in the blockchain network, and a directional carbon compensation transaction request is generated and executed according to the renewable energy credit; the directional carbon compensation transaction request is encrypted and stored in the blockchain distributed ledger to form a full-chain evidence covering the traceability of membrane material production, operation carbon emission tracking and compensation transaction verification, and the unique identification code of the full-chain evidence is written into the membrane component 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 instantaneous membrane flux 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; and 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, combined with the nonlinear response correction of salt concentration to flux attenuation, a weight optimization model of pollutant deposition to carbon emission baseline is established; the coupling degree of membrane anti-pollution layer degradation rate and transport carbon emission coefficient is synchronously integrated, and the dynamic influence factor of inlet salt concentration on membrane surface charge is 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 carbon emission baseline is achieved; through the coupling modeling of anti-pollution layer degradation and transport carbon emissions, the additional carbon emissions of membrane material loss are quantified; combined with the real-time impact of salt concentration on charge density, a multi-factor linkage carbon emission increment accurate accounting mechanism is formed, which ultimately achieves 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 clearly understood in 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 paying any creative work.

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

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

[0066] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application.

[0067] In some processes described in the specification, claims, and the above-mentioned accompanying drawings of this application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The operation numbers such as 101 and 102 are only used to distinguish different operations, and the numbers themselves do not represent any execution order. 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 such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0068] By integrating the carbon emission factor data of the whole life cycle of membrane material production, transportation, and degradation, a dynamic mapping relationship between the membrane module type and the 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 the membrane flux and the changes in the salt rejection efficiency in real time. The degree of membrane performance degradation is quantified through the correlation model between the salt rejection efficiency fluctuation value and the pollutant deposition amount; a multi-parameter dynamic calculation model is constructed based on the operation duration, the influent salt concentration, and the carbon footprint factor set, and the performance degradation indicators such as the membrane flux decay rate and the degradation of the anti-fouling layer are converted into real-time carbon emission increments; a blockchain smart contract trigger mechanism is designed, and through the binding and matching of the device geographical location and the green power credit limit, automatic carbon compensation is realized when the carbon emission increment exceeds the standard; finally, the blockchain distributed ledger is used to encrypt and store the compensation transaction data, and the full-chain carbon footprint traceability is realized with the help of the chip-level identification code, forming a closed-loop of the whole-link carbon emission management covering "data collection, dynamic accounting, intelligent compensation, and trusted evidence storage".

[0069] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0070] Figure 1 A flowchart of a method for carbon emission accounting and management of a seawater desalination system based on blockchain is provided for the embodiments of this application, as Figure 1 shown, and the method includes:

[0071] 101. Integrate the carbon emission correlation data throughout the life cycle of the seawater desalination membrane material to form a set of carbon footprint factors. Among them, the carbon dioxide equivalent value per unit area in the production stage matches the corresponding carbon emission quantification relationship according to the membrane module type;

[0072] In this step, the set of carbon footprint factors refers to a carbon emission quantification parameter system constructed for the entire life cycle (production, transportation, operation, and disposal) of the seawater desalination membrane material, including the physical property parameters and environmental correction coefficients related to the carbon emission per unit area / volume of the membrane module at different stages.

[0073] In the embodiments of this application, a multi-dimensional carbon emission parameter space is constructed through a life cycle assessment (LCA) model. The random forest algorithm is used to analyze the importance of features of production data such as the sintering temperature curve and chemical solvent residue amount of the membrane material, and the process parameters strongly related to carbon emissions are screened out. The hypercube sampling technique is used to perform multi-dimensional interpolation on the discrete temperature and humidity data in the transportation stage and the measured values of the degradation rate in the disposal stage to generate a continuous carbon footprint factor surface. For different membrane module types, the Bayesian optimization algorithm is used to match the porosity distribution characteristics with the carbon emission quantification relationship, and finally a set of carbon footprint factors including the physical properties of the membrane material and the environmental adaptation coefficient is formed. The carbon dioxide equivalent value per unit area in the production stage is calculated through an 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] Suppose a seawater desalination plant uses the DuPont FilmTec SW30XLE-400 reverse osmosis membrane module, and its production stage sintering process data includes a three-stage temperature curve (heating rate 15°C / min, peak temperature 372°C ± 3°C, holding time 3.2 h). The random forest algorithm is used to screen the features of the membrane material crystallinity gradient data (XRD detection half-peak width 0.32°), and the thermal efficiency coefficient of the sintering furnace (0.68) is determined as the key factor for carbon emissions. The hypercube sampling technique is used to perform three-dimensional interpolation on the temperature and humidity data in the transportation stage (the proportion of the duration with humidity > 90% during sea transportation is 42%) and the biodegradation experiment data in the disposal stage (the annual average mass loss rate is 1.2% under 30°C seawater immersion) to generate a set of carbon footprint factors. Finally, the carbon emission value corresponding to the 0.25 μm porosity characteristic of this membrane module in the production stage is 1.35 kg CO2e / m².

[0075] 102. Fix a graphene sensor array on the membrane surface of the reverse osmosis membrane module, collect the instantaneous change rate of the membrane flux and the fluctuation value of the salt rejection efficiency in real time, and determine the deposition amount of pollutants on the membrane surface based on the fluctuation value of the salt rejection efficiency;

[0076] In this step, the graphene sensor array consists of a sensing network composed of honeycomb-arranged graphene field-effect transistors. By monitoring the current impedance changes and ion adsorption characteristic spectra in the microregions on the membrane surface, spatial heterogeneity data of the membrane flux decay rate and the salt rejection efficiency fluctuation value are captured in real time.

[0077] In the embodiment of this application, graphene sensing units are prepared on the surface of the polyamide active layer by using the chemical vapor deposition (CVD) process, and the sensing node spacing (adjustable from 200 to 500 μm) is adjusted based on the results of the flow channel hydrodynamics simulation. The instantaneous change rate of the membrane flux is collected by the time-resolved impedance spectroscopy (TRIS) technique, and the sampling frequency is set to 10 kHz to capture the flux fluctuations at the nanosecond level. The salt rejection efficiency fluctuation value is obtained through in-situ Raman spectroscopy analysis, and the integral intensity calculation is performed for the adsorption characteristic peak (wave number 258 cm⁻¹) of Cl⁻ ions on the membrane surface. The wavelet packet decomposition algorithm is used to filter out the high-frequency mechanical vibration noise (>1 MHz), and the pollution characteristic signals in the frequency band of 0.1 - 10 kHz are retained. A non-linear mapping model between the salt rejection efficiency fluctuation value and the pollutant deposition thickness is established by using the convolutional neural network (CNN). The input layer contains a spatio-temporal feature tensor with 16 channels, and finally, the pollutant deposition amount on the membrane surface is determined according to the salt rejection efficiency fluctuation value.

[0078] For example, continuing with the above example, a 128-channel graphene sensor array is prepared on the FilmTec membrane surface by using the mask-assisted CVD process, and the unit spacing is adjusted to 250 - 480 μm according to the distribution of the flow channel vortex intensity. The instantaneous change data of the flux are captured by the time-resolved impedance spectroscopy technique (sampling rate 12.8 kHz). When the inlet water pressure is 6.5 MPa, the flux baseline value is detected to be 32 L / m² / h, and the instantaneous fluctuation peak-to-peak value reaches ±8%. The characteristic doublet (258 cm⁻¹ and 432 cm⁻¹) formed by Cl⁻ ions on the membrane surface is collected by using a confocal Raman spectroscopy system (laser wavelength 785 nm), and the pump vibration noise above 1.2 MHz is filtered out by wavelet packet decomposition. The processed spectral data are input into a pre-trained 3D-ResNet model (input dimension 16×16×64), and the pollutant deposition amount on the membrane surface in the northwest quadrant is output as 5.2 μm.

[0079] 103. According to the instantaneous change rate of the membrane flux, the pollutant deposition amount on the membrane surface, and the set of carbon footprint factors, combined with the operation duration of the seawater desalination device and the inlet water salt concentration data, dynamically calculate the real-time carbon emission increment generated due to the deterioration of the membrane performance;

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

[0081] In the embodiment of the present application, multi-source heterogeneous data is fused based on the extended Kalman filter (EKF): the pollutant deposition thickness distribution data (spatial resolution 50μm) 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, and the parameters are calibrated by high-pressure nuclear magnetic resonance (HP-NMR) experiments. A fractional differential equation is introduced to construct an anti-pollution layer degradation model, in which the time fractional order α is dynamically adjusted by the operating time data (α=0.83 when >5000h). Finally, the probability density distribution of carbon emission increment is generated by 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 the above example, when the device has been running for 7200 hours, the extended Kalman filter integrates the following data: the transport phase coefficient in the carbon footprint factor set is 0.018kg CO2e / (m·h), the 5.2μm deposition thickness output in step 102, and the inlet salt concentration is 41000ppm. The dynamic correction term is calculated using the salt concentration and osmotic pressure curve calibrated by high-pressure nuclear magnetic resonance (non-linear coefficient γ=1.73). The degradation of the anti-pollution layer is simulated by a fractional differential equation (α=0.79), and the probability distribution of carbon emission increment is generated by combining Monte Carlo importance sampling (5000 iterations). The final output is a real-time carbon emission increment of 3.1kg CO2e / h, of which membrane pollution contributes 62% and material degradation contributes 38%.

[0083] 104. When the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credits bound to the geographical location of the desalination device are automatically matched through the smart contract in the blockchain network, and a targeted carbon compensation transaction request is generated and executed according to the renewable energy credits;

[0084] In this step, the targeted carbon offset transaction request contains standardized transaction instructions of 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 the embodiment of the present application, a Delaunay triangulation model of the geographic grid is constructed, and the GPS coordinates of the desalination device are mapped to a 500m×500m grid unit. 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 through the approximate nearest neighbor (ANN) algorithm. Among them, the smart contract uses zero-knowledge proof (zk-SNARK) to verify the spatiotemporal validity of carbon emission increment data, and completes the on-chain and off-chain data synchronization through the atomic exchange protocol; the credit weight calculation introduces an attention mechanism to dynamically correct the wind power prediction error (the weight is reduced to 0.6 when RMSE>15%) and the photovoltaic irradiance monitoring delay (the weight is reduced to 0.4 when>5min); finally, a directional carbon compensation transaction request is generated and executed.

[0086] For example, continuing the above example, the blockchain smart contract parses that the device is located in the Fujairah grid in the United Arab Emirates (coded AE-FJ17), and retrieves the remaining wind power credit quota of 58MWh and photovoltaic credit of 92MWh in the area on that day through the improved R* tree index. The attention mechanism dynamically adjusts the credit weight according to the weather forecast data (wind speed prediction error RMSE=12.7%): wind power weight 0.68, photovoltaic weight 0.89. After the zero-knowledge proof verifies the validity of the incremental carbon emission data, a targeted carbon compensation transaction request is generated and executed to purchase 4.8MWh of wind power credit and 3.2MWh of photovoltaic credit, generating a transaction hash value of 0x4d9a...e7f1, and the on-chain confirmation delay is <1.2 seconds.

[0087] 105. Encrypt and store the targeted carbon compensation transaction request 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 write the unique identification code of the full-chain evidence into the membrane component identification chip.

[0088] In this step, full-chain evidence storage refers to an unalterable data chain that fully records the membrane material production traceability identification, the carbon emission tracking log during the operation phase, and the carbon compensation transaction certificate in the blockchain distributed ledger. The unique identification code is an encrypted string generated by the blockchain hash value, the membrane component serial number, and the transaction timestamp, and is permanently stored in the membrane component's identification chip through physical burning.

[0089] In the embodiments of the present application, the IPFS protocol is adopted to fragment and store the original carbon footprint data (fragment size: 256 KB), and the Plasma framework is used to build a side chain to store high-frequency sensor data. The unique identification code for the deposit is generated by the Keccak-256 algorithm and includes the membrane module serial number (16 bits), the blockchain height (64 bits), and the compressed sensing fingerprint (128 bits). The physical writing process uses femtosecond laser ablation technology to form a micron-level QR code array on the surface of the silicon nitride chip. When reading, the deposit information is parsed through a confocal Raman microscope (wavelength: 532 nm). Cross-chain verification uses the light node SPV protocol, and after quickly locating the target transaction through a Bloom filter (false positive rate < 0.1%), a full-chain deposit is formed.

[0090] For example, continuing with the previous example, the directional carbon compensation transaction data is stored in fragments through IPFS (CID: bafkreiabc...vq), and the Plasma side chain architecture is used to compress 1200 sensor data per second into a Merkle root hash. The unique identification code for the deposit "SW30XLE-FJ17-0x4d9a" is engraved on the surface of the membrane module chip through a femtosecond laser micromachining system (pulse energy: 0.8 mJ, wavelength: 1030 nm) to form a QR code array with a depth of 2.5 μm. During the third-party audit, a terahertz time-domain spectrometer (resolution: 6 μm) is used to read the chip data, and cross-chain verification is completed within 1.8 seconds through the light node SPV protocol. The generated full-chain deposit information includes the production batch number, the cumulative carbon emissions during 7200 hours of operation (22.3 t CO2e), and the compensation transaction validity mark.

[0091] Steps 101-105 realize the accurate accounting and closed-loop management of the whole life cycle of the carbon emissions of the seawater desalination system through five core technologies: the construction of the carbon footprint factor set, the deployment of the graphene sensing array, the calculation of the dynamic incremental model, the execution of the blockchain smart contract, and the writing of the full-chain deposit. It breaks through the dynamic association of the physical degradation mechanism of the membrane material with the virtual carbon data, solves the core problems in traditional methods such as the disconnection between the static database and the dynamic operation, insufficient data credibility, and lagging compensation mechanism, and provides an innovative technical path for the carbon footprint management of high-energy-consuming industrial facilities.

[0092] To solve the problem of the disconnection between the dynamic performance degradation and the static carbon factor library in the carbon emissions accounting of the seawater desalination system and further improve the spatio-temporal resolution accuracy and physical mechanism coupling of the carbon emissions increment calculation, an innovative path based on the deep fusion of multi-source heterogeneous data is proposed. In some embodiments, the real-time carbon emissions increment caused by the degradation of the membrane performance is dynamically calculated according to the instantaneous change rate of the membrane flux, the deposition amount of pollutants on the membrane surface, and the carbon footprint factor set, in combination with the operation duration of the seawater desalination device and the inlet salt concentration data, including:

[0093] 201. Extract the spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, and combine it with the carbon emission coefficient in the carbon footprint factor set during the membrane material degradation stage to generate 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 regions on the reverse osmosis membrane surface, including the spatial difference value of the flux decay gradient.

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

[0096] In the embodiments of the present application, the random forest algorithm is used to rank the feature importance of the membrane flux spatio-temporal data collected by the graphene sensor array, and screen out the spatial heterogeneity indicators strongly related to carbon emissions (such as regions where the standard deviation of the flux decay gradient > 15%). A non-linear mapping surface between the degradation stage coefficient (such as 0.025 kg CO2e / (m²·h)) in the carbon footprint factor set and the flux decay rate is constructed through Gaussian process regression. The input parameters include the porosity distribution of the membrane module (0.1 - 0.3 μm) and the operating pressure fluctuation range (5 - 8 MPa). The tensor decomposition technology is used to reduce the dimensionality of the multi-dimensional data to a three-dimensional principal component space, and a dynamic correlation baseline (dimension 16×16) represented by a weight matrix is generated, where the weight ratio of the flux decay rate accounts for 65% and the carbon footprint factor accounts for 35%.

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

[0098] In step 202, the non-linear response relationship refers to the non-proportional correlation between the change in the inlet salt concentration and the instantaneous rate of the membrane flux, manifested as the salt concentration threshold effect.

[0099] In the embodiments of the present application, a topological relationship model of salt concentration and flux decay is constructed based on a graph convolutional network (GCN). The node features include salt concentration sampling values (at 10-minute intervals), membrane surface channel geometric parameters (curvature radius of 0.5 - 2 mm), and pollutant deposition thickness (0 - 10 μm). Through an attention mechanism, correction coefficients (weights of 0.3 - 0.7) for flux decay in different salt concentration intervals (such as 30000 - 40000 ppm) are dynamically allocated. The non-local mean filtering algorithm is used to eliminate high-frequency noise (frequency band > 100 Hz) in spatially heterogeneous data and retain low-frequency feature signals related to pollutant deposition (frequency band of 1 - 10 Hz). Finally, the weight distribution of the dynamic correlation baseline is corrected through a generative adversarial network (GAN), where the weight in high-pollution areas (deposition amount > 5 μm) is increased to 1.8 times the original value.

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

[0101] In step 203, the coupling degree characterizes the synergy intensity index between the reduction rate of the anti-pollution layer thickness of the membrane material and the change rate of the carbon emission coefficient in the transportation stage. The additional carbon emission correction value for membrane performance degradation is the incremental part exceeding the baseline carbon emission value caused by membrane performance degradation, and is generated through quantification of the coupling degree.

[0102] In the embodiments of the present application, a long short-term memory network (LSTM) is used to analyze the degradation trend of the anti-pollution layer in the operation duration data (> 5000 h). The input features include extreme environmental temperature (25 - 45 °C), inlet water pH fluctuation (6.8 - 7.5), and backwashing period (4 - 6 h). The dynamic coupling degree between the carbon emission coefficient in the transportation stage and the degradation rate is calculated through a particle swarm optimization algorithm (PSO). The constraint conditions include transportation distance (coupling degree increases by 20% when > 2000 km) and environmental humidity (coupling degree decays by 15% when > 80%). The Monte Carlo tree search (MCTS) is used to generate the probability distribution of the correction value, and the 95th percentile is selected as the additional carbon emission correction value for membrane performance degradation for output.

[0103] 204. Integrate the corrected weight distribution and the additional carbon emission correction value for membrane performance degradation, and dynamically calculate the real-time carbon emission increment caused by membrane performance degradation in combination with the dynamic influence factor of the inlet salt concentration data on the membrane surface charge density.

[0104] In step 204, the dynamic influence factor reflects the dynamic adjustment coefficient of the influence of the change in inlet salt concentration on the membrane surface charge density (such as -30 mV to -50 mV). The real-time carbon emission increment is the final carbon emission over-standard value output after integrating the weight distribution correction value and the additional correction value.

[0105] In the embodiments of the present application, a multi-physical field coupling model is constructed. The charge density data (measured by a zeta potentiometer) is spatially and temporally aligned with the influent salt concentration data. The finite element analysis (FEA) is used to calculate the adsorption energy gradient (0.5 - 2.8 eV) of salt ions (Na⁺, Cl⁻) on the membrane surface. The fusion ratio of the weight distribution and the additional correction value is dynamically adjusted through the Bayesian optimization algorithm (weight ratio 55% vs 45%). The constraint conditions include the dielectric constant of the membrane material (2.5 - 3.2) and the operating voltage fluctuation (24V ± 5%). Finally, the genetic algorithm (GA) is used to globally optimize the real-time carbon emission increment caused by membrane performance degradation. The fitness function includes the constraint of the carbon emission increment threshold (such as ≤ 3.5 kg CO2e / h) and the energy consumption cost weight (0.6).

[0106] The following is a specific example:

[0107] A certain SWRO seawater desalination plant in the Middle East uses SWC5 membrane modules (initial flux 35 L / m² / h, anti-fouling layer thickness 120 μm). After operating for 6500 hours, the following steps are carried out. First, according to step 201, the graphene sensor detects that the flux decay gradient in the northeast quadrant of the membrane surface reaches 22% (baseline value ± 8%). Combining 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 is generated; according to step 202, the influent salt concentration rises to 42000 ppm, and the GCN model outputs a salt concentration correction coefficient of 0.68. The weight of the pollutant deposition area of 5.8 μm is increased to 1.7 times, and the corrected baseline value rises to 2.9 kg CO2e / h; through step 203, the LSTM is used to predict the degradation rate of the anti-fouling layer as 0.025 μm / h, and the coupling degree with the transportation stage coefficient (0.021 kg CO2e / km) calculated by PSO is 1.32, generating an additional correction value of 0.8 kg CO2e / h; finally, according to step 204, the charge density dynamic factor is calculated as 1.15 (when the salt concentration > 40000 ppm), and the Bayesian optimization fuses the weights to output a real-time carbon emission increment of 3.5 kg CO2e / h.

[0108] Steps 201 - 204 achieve millimeter-level spatial resolution and minute-level dynamic update of the carbon emission increment in the seawater desalination system through four-order innovations of spatial heterogeneity analysis, non-linear response modeling, degradation and transportation coupling degree calculation, and multi-physical field fusion optimization, solving the problems of linear hypothesis errors between membrane performance degradation and carbon emissions, lag in salt concentration mutation response, and cross-stage data islands in traditional methods, providing a carbon accounting closed-loop system that links the entire life cycle and real-time operation for industrial-scale seawater desalination facilities.

[0109] To break through the bottleneck of insufficient correlation between the dynamic degradation of the anti-fouling layer of membrane materials and the evolution of carbon footprint during the transportation stage in the carbon emission accounting of seawater desalination systems, and further improve the cross-stage data coupling accuracy and extreme environment adaptability, an innovative path based on spatio-temporal dynamic coupling is proposed to solve the defects of static transportation carbon emission coefficients and the disconnection between membrane performance degradation and transportation history in traditional methods. In some embodiments, based on the operation duration data of the seawater desalination device, the coupling degree between the degradation rate of the anti-fouling layer of the membrane material and the transportation stage carbon emission coefficient in the carbon footprint factor set is calculated to generate a correction value for additional carbon emissions due to membrane performance degradation, including:

[0110] 301. Extract the extreme value distribution of environmental temperature and the fluctuation frequency of inlet water pressure in the operation duration data of the seawater desalination device, and combine with the initial performance parameters of the anti-fouling layer in the membrane module factory inspection report to generate a set of dynamic influence factors for the degradation rate of the anti-fouling layer of the membrane material;

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

[0112] In the embodiments of the present application, the fuzzy C-means clustering algorithm is used to divide the multi-modal distribution of the extreme values of environmental temperature (such as the daily maximum temperature of 45°C ± 3°C) in the operation duration data to generate a temperature sensitivity level (high / medium / low). The main frequency component (0.1 - 5 Hz) of the inlet water pressure fluctuation is extracted by wavelet transform, and combined with the initial thickness (such as 120 μm) and porosity (0.25 μm) of the anti-fouling layer detected during the factory inspection of the membrane module, a degradation rate prediction model based on decision tree regression is constructed. The input parameters include the proportion of the duration of extreme temperature (the period > 40°C accounts for 28%), the peak frequency of pressure fluctuation (2.3 Hz), and the initial performance parameters, and the output is a set of dynamic influence factors.

[0113] 302. Based on the spatio-temporal distribution characteristics of the transportation stage carbon emission coefficient in the carbon footprint factor set, through the correlation between the environmental temperature and humidity gradient in the transportation route and the moisture absorption expansion rate of the membrane material, establish a dynamic attenuation model between the transportation stage carbon emission coefficient and the initial performance parameters of the anti-fouling layer;

[0114] In step 302, the dynamic attenuation model characterizes the mathematical relationship between the transportation stage carbon emission coefficient and the initial performance parameters of the anti-fouling layer of the membrane material that decays with the environment.

[0115] In the embodiments of the present application, based on the GPS trajectory data of the transportation route, the Kriging interpolation method is used to construct the spatial distribution surface of environmental temperature and humidity (resolution 1 km × 1 km), and the Pearson correlation coefficient (r = 0.76) between the moisture absorption expansion rate of the membrane material (expansion amount per hour 0.02% - 0.15%) and the temperature and humidity gradient is extracted. A multi-dimensional regression model is constructed by a support vector machine, and the input parameters include the average humidity in the transportation stage (82% ± 7%), the extreme value of the day-night temperature difference (12 °C), and the initial thickness of the anti-pollution layer, and a dynamic attenuation model is output. And the Bayesian posterior estimation of the model parameters is carried out by the Markov chain Monte Carlo (MCMC) method.

[0116] 303. Introduce the non-linear amplification coefficient of the crystal defect density of the membrane material in the high-temperature and high-salt environment on the degradation rate of the anti-pollution layer of the membrane material, and combine the set of dynamic influence factors to calculate the spatio-temporal coupling degree between the degradation rate of the anti-pollution layer of the membrane material and the carbon emission coefficient in the transportation stage;

[0117] In step 303, the non-linear amplification coefficient is a non-linear enhancement factor reflecting the influence of the crystal defect density of the membrane material in the high-temperature and high-salt environment on the degradation rate of the anti-pollution layer. The spatio-temporal coupling degree is an index quantifying the synergy intensity between the degradation rate of the anti-pollution layer and the carbon emission coefficient in the transportation stage in the spatio-temporal dimension.

[0118] In the embodiments of the present application, a scanning electron microscope (SEM) is used to obtain the distribution of the crystal defect density of the membrane material (0 - 15 per μm²), and the spatial complexity of the defects is characterized by fractal dimension calculation (D = 1.82 ± 0.03). A prediction model of the amplification coefficient based on a radial basis function neural network (RBFNN) is constructed, and the input parameters include the salt concentration in the operating environment (38000 - 45000 ppm), the temperature gradient (ΔT = 8 °C / h), and the defect density. The firefly optimization algorithm (FA) is used to solve the spatio-temporal coupling degree between the degradation rate and the transportation carbon emission coefficient, and the fitness function includes the transportation distance (the weight is increased by 25% when > 3000 km) and the defect density threshold (the coupling degree non-linearly increases by 1.8 times when > 10 per μm²).

[0119] 304. Based on the spatio-temporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane module, a correction value for the additional carbon emission due to the deterioration of the membrane performance is generated through the product relationship between the degradation rate of the anti-pollution layer of the membrane material and the attenuation amount of the carbon emission coefficient in the transportation stage.

[0120] In step 304, the salt concentration and temperature gradient data refer to the composite parameter composed 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 the embodiments of the present application, the multi-physical field coupling simulation is used to calculate the influence of the salt concentration gradient (ΔC = 2000 ppm / m) on the electrochemical potential of the membrane surface (-35 mV to -58 mV). Combining with the temperature gradient data (ΔT = 2.3 °C / m) collected by the infrared thermal imager, an environmental correction factor matrix (16×16) with spatio-temporal coupling degree is generated. The product relationship weight between the degradation rate (such as 0.026 μm / h) and the reduction amount of transportation carbon emissions (such as 0.019 kg CO2e / km) is optimized by the quantum genetic algorithm (QGA). The constraint conditions include the Young's modulus of the membrane material (2.4 GPa) and the operating voltage stability (fluctuation <5%). Finally, the additional carbon emissions correction value for membrane performance degradation is output.

[0122] The following is a specific example:

[0123] A SWRO plant in the Yanbu Industrial Zone, Saudi Arabia, uses PROC10 membrane modules (initial anti-fouling layer thickness of 115 μm and transportation distance of 3850 km). First, based on step 301, the environmental temperature extreme value analysis shows that the average daily high temperature is 46 °C for 6.2 hours, and the main frequency of the inlet water pressure fluctuation is 2.8 Hz. The decision tree model outputs a high temperature factor of 1.45 and a pressure factor of 0.93; in step 302, the temperature and humidity interpolation surface of the transportation route shows the highest humidity of 91%. The support vector machine model calculates the reduction rate of the transportation carbon emission coefficient to be 5.3%, and the value in the northeast region of the dynamic attenuation coefficient matrix reaches 0.78; in step 303, the SEM detects the crystal defect density of 12 per μm², the RBFNN model outputs a non-linear amplification factor of 1.65, and the firefly algorithm calculates the spatio-temporal coupling degree of 1.72; through step 304, the salt concentration gradient ΔC = 2500 ppm / m and the temperature gradient ΔT = 2.8 °C / m are obtained. After quantum genetic optimization, an additional carbon emissions correction value for membrane performance degradation of 1.8 kg CO2e / h is generated.

[0124] Steps 301-304 construct a dynamic correction system for the carbon emissions of seawater desalination with cross-stage and multi-physical field coupling through the environmental temperature extreme value clustering analysis, transportation temperature and humidity spatial interpolation modeling, crystal defect fractal quantification, and quantum optimization algorithm. Under complex working conditions such as long-distance transportation and high temperature and high salt, the correlation accuracy between the degradation rate of the anti-fouling layer and transportation carbon emissions is significantly improved, effectively solving industry problems such as the lack of transportation environment memory effect and misjudgment of defect accelerated degradation in traditional methods. By integrating transportation historical data and real-time operation parameters, the environmental adaptive generation of the carbon emissions correction value for membrane performance degradation is realized, providing an industrial-level carbon accounting solution with millimeter-level spatial resolution and hourly timeliness for high-volatility seawater desalination scenarios, greatly improving the integrity of the full-life cycle carbon emissions tracking and the reliability of the compensation trading trigger mechanism.

[0125] To break through the bottlenecks of data fragmentation in the "production, transportation, and disposal stages" and insufficient service environment adaptability in the construction of the carbon footprint factor library for seawater desalination systems, an innovative path of full-life-cycle data fusion and dynamic correction is proposed to solve problems such as the static nature of traditional carbon factor libraries and the disconnection of environmental parameters. In some embodiments, the carbon emission correlation data throughout the life cycle of the seawater desalination membrane material is integrated to form a set of carbon footprint factors. The carbon dioxide equivalent value per unit area in the production stage matches the corresponding carbon emission quantification relationship according to the membrane module type, including:

[0126] 401. Obtain the original production data of the membrane material, extract multi-dimensional carbon emission parameters of the sintering temperature curve and chemical solvent consumption of the membrane material, and divide the multi-dimensional carbon emission parameters into discretized carbon footprint units according to the porosity distribution characteristics and anti-fouling layer thickness parameters corresponding to the membrane module type;

[0127] In step 401, the multi-dimensional carbon emission parameters include the peak / constant temperature section data of the sintering temperature curve of the membrane material and the carbon emission correlation parameters in the production stage of the unit consumption of chemical solvents. The discretized carbon footprint unit is the basic unit for calculating carbon emissions divided according to the membrane module porosity distribution and anti-fouling layer thickness.

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

[0129] 402. For the production stage data in the discretized carbon footprint units, 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 batches of membrane modules through the coupling relationship between the change gradient of the membrane material crystallinity and the sintering energy consumption;

[0130] In step 402, the dynamic mapping rule is to establish a non-linear correspondence between the membrane module type and the carbon emission value per unit area, which is dynamically adjusted with the crystallinity gradient and sintering energy consumption. The carbon emission deviation is the difference in carbon emission values caused by process fluctuations in different production batches.

[0131] In the embodiments of the present application, based on X-ray diffraction (XRD), the crystallinity gradient data of the membrane material (full width at half maximum: 0.15° - 0.45°) is obtained, and a Gaussian mixture model (GMM) is used to construct the probability distribution surface of the crystallinity change gradient of the membrane material and the sintering energy consumption (kWh / m²). The best mapping relationship of different membrane module types (such as spiral wound / plate type) is inferred through a Bayesian network, and the input nodes include parameters such as the thermal efficiency of the sintering furnace (65 - 82%) and the consumption of inert gas (0.5 - 1.2 L / m²). The particle swarm optimization (PSO) algorithm is used to correct the deviation between batches, and the constraint condition is the threshold of carbon emission difference between adjacent batches (<5%), and finally a dynamic mapping rule is generated.

[0132] 403. Integrate the ambient temperature and humidity sensing data in the transportation stage of the membrane material and the measured degradation rate data in the waste stage, and expand the discretized carbon footprint unit into a continuous carbon footprint factor including the production, transportation, and waste stages. The continuous carbon footprint factor generates an independent data index identifier according to the membrane module serial number and the dynamic mapping rule;

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

[0134] In the embodiments of the present application, a LoRa wireless sensor network is deployed to collect the temperature and humidity data in the transportation stage (sampling interval: 5 minutes), combined with the degradation rate data of the seawater immersion experiment in the waste stage (annual average mass loss rate: 0 - 2%), and a three-stage data fusion model is constructed using a temporal convolutional network (TCN). The discretized carbon footprint unit is expanded into a continuous factor through a hierarchical clustering algorithm (HCA), and the feature dimension is increased to 64 dimensions. An improved hashing algorithm is used to generate an independent data index identifier, including the membrane module serial number (16 bits), the production date (Unix timestamp), and the dynamic mapping version number (v1.2.3), realizing a fast retrieval of 100,000 data per second.

[0135] 404. 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, perform service adaptability correction on the continuous carbon footprint factor to generate a set of carbon footprint factors including the physical property parameters of the membrane material and the environmental adaptation coefficient.

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

[0137] In the embodiments 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 latent 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 daily average temperature fluctuation range (ΔT = 5 - 15 °C), and the thermal expansion coefficient of the membrane material (1.2 - 2.8×10⁻ 5 / °C). The tensor splicing technology is used to fuse the environmental adaptation coefficient and the continuous carbon footprint factor for service adaptability correction, and finally a set of carbon footprint factors containing 128 physical characteristic parameters is generated.

[0138] The following is a specific example:

[0139] Suppose a seawater desalination plant in Jebel Ali, UAE, uses TM820D - 400 membrane modules (porosity 0.22 μm ± 0.03, anti - pollution layer thickness 135 μm). Through principal component analysis of the sintering temperature curve in step 401, the peak temperature of 382 °C and the constant temperature time of 2.8 h are extracted. Combining with the solvent consumption of 0.8 L / m², 16 discrete carbon footprint units are generated by K - means clustering; in step 402, the full width at half maximum of the crystallinity detected by XRD is 0.28°, and the GMM model shows that the optimal range of sintering energy consumption is 18 - 22 kWh / m². After PSO correction, the carbon emission difference between batches is reduced to 3.7%; in step 403, during the transportation stage, the humidity > 90% is monitored for 120 hours, and the TCN model fuses 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 analyzes the salinity fluctuation range of the plant as 38,000 - 43,000 ppm and the daily average ΔT = 12 °C. The random forest calculates the environmental adaptation coefficient of 1.15, and the final set of carbon footprint factors contains 128 parameters such as the thermal conductivity of the membrane material and the ion adsorption rate.

[0140] Steps 401 - 404 construct the world's first carbon footprint factor library covering the entire life cycle of membrane materials and with environmental self - adaptation through principal component feature extraction, Gaussian mixture modeling, temporal convolutional fusion, and variational auto - encoding optimization. In a complex operating environment, it significantly improves the carbon accounting accuracy and cross - stage data collaboration ability, effectively solves the core defects such as the fragmentation of production - transportation data and the lag in service environment response in traditional methods, provides a full - scale carbon management solution for the seawater desalination system from nanoscale material properties to kilometer - scale transportation paths, and realizes the dynamic and accurate tracking of carbon footprint factors and real - time environmental adaptation.

[0141] To break through the technical bottlenecks of insufficient spatial resolution and poor dynamic correlation of pollutant deposition in the monitoring of membrane fouling in seawater desalination systems, an innovative path based on graphene multimodal sensing and three-dimensional thermal modeling is proposed to solve the defects such as single-point monitoring blind spots and linearization of pollution thickness estimation in traditional methods, and to achieve holographic monitoring of the membrane surface pollution state and precise backwashing control. In some embodiments, 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 fluctuation value of salt rejection efficiency in real time. Determining the amount of pollutant deposition on the membrane surface based on the fluctuation value of salt rejection efficiency includes:

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

[0143] In step 501, the honeycomb topology refers to an array structure in which graphene sensing units are arranged in a regular hexagon geometric pattern on the membrane surface, with optimal spatial coverage density and signal anti-interference characteristics. The embedded signal acquisition module is a high-speed signal processing unit integrated in the end cap of the membrane module to realize real-time conversion and preprocessing of sensing data.

[0144] In the embodiments of the present application, based on computational fluid dynamics (CFD) simulation of the vortex distribution characteristics (Reynolds number Re = 1200 - 2500) in the reverse osmosis membrane flow channel, a high flow velocity gradient region (flow velocity difference > 0.8 m / s) is determined as the key monitoring site for the sensing array. A graphene field-effect transistor array is fabricated on the polyamide active layer surface using photolithography technology, with a unit size of 50 μm × 50 μm, and the spacing density is dynamically adjusted to 300 - 600 μm according to the flow channel width (0.5 - 2 mm). The 256-channel electrode leads are connected to the FPGA-based embedded signal acquisition module in the end cap through the gold ball bonding process, with the signal transmission delay controlled within < 2 μs and the synchronous trigger accuracy reaching ±5 ns.

[0145] 502. Based on the adjusted spacing density, synchronously capture the instantaneous change rate of membrane flux and the fluctuation value of salt rejection efficiency through the periodic scanning mode of the graphene sensing array, and synchronously collect the micro-region current signal and ion adsorption characteristic spectrum on the membrane surface within a single scanning cycle, filter out the high-frequency noise in the signal and retain the effective data associated with the membrane fouling characteristic frequency band;

[0146] In step 502, the periodic scanning mode is a triggering mechanism for cyclically collecting data of each sensing unit at a fixed time interval (such as 10 ms). The membrane fouling characteristic frequency band is a specific signal frequency range strongly correlated with the pollutant deposition process.

[0147] In the embodiments of the present application, a time-frequency joint analysis system is constructed for periodic scanning. The original signal (sampling rate 1 MHz) is divided into 32 sub-bands by using the wavelet packet decomposition algorithm. The intensity change of the Cl⁻ ion adsorption characteristic spectrum (Raman shift 258 cm⁻¹ ± 3 cm⁻¹) is identified through a convolutional neural network (CNN). The input layer includes the time-domain waveform, frequency-domain energy, and spatial position encoding. An IIR digital filter bank is designed with a cut-off frequency set to 100 Hz (stop-band attenuation > 60 dB) to retain the low-frequency components related to pollutant deposition. Effective data segments are extracted through adaptive threshold segmentation, and the signal-to-noise ratio (SNR) is increased to above 32 dB.

[0148] 503. Construct a dynamic correlation model of membrane surface pollutant deposition based on the time-domain change trend of the salt rejection efficiency fluctuation value. Combine the surface roughness parameters of the membrane module and the operating pressure gradient data to convert the attenuation slope of the salt rejection efficiency fluctuation value into the pollutant deposition thickness distribution value.

[0149] In step 503, the time-domain change trend is the curve morphological feature formed by the salt rejection efficiency fluctuation value over time.

[0150] In the embodiments of the present application, the surface roughness parameters of the membrane module (fractal dimension D = 2.15 - 2.35) are analyzed using fractal geometry theory, and a particle swarm optimization algorithm improved based on Lévy flight (LFPSO) is constructed to solve the deposition thickness distribution. The input parameters include the operating pressure gradient (0.2 - 0.8 MPa / m), attenuation slope (0.5 - 3% / h), and surface contact angle (55 - 75°). The stress distribution of pollutant deposition (0 - 15 kPa) is calculated through finite element multi-physics field coupling. Combining the proportion of pollutant components calibrated by X-ray photoelectron spectroscopy (XPS) (such as 65% CaCO3, 22% SiO2), the pollutant deposition thickness distribution value is output.

[0151] 504. According to the spatial heterogeneity distribution of the instantaneous change rate of the membrane flux and the pollutant deposition thickness distribution value, construct a three-dimensional thermal map of the pollutant accumulation hot spot area on the membrane surface flow channel section, and associate the backwashing cycle optimization parameters of the current operating stage to determine the amount of pollutant deposition on the membrane surface.

[0152] In step 504, the spatial heterogeneity distribution is the non-uniformity difference feature presented by the instantaneous rate of the membrane flux in different surface areas. The backwashing cycle optimization parameters are control parameters such as the backwashing trigger time and pressure gradient dynamically adjusted according to the degree of pollutant accumulation.

[0153] In the embodiments of the present application, a spatial correlation model between flux and pollution thickness is constructed based on the Kriging interpolation method, and a three-dimensional heat map (color scale resolution 0.1μm) of 500×500 pixels is generated. The backwashing strategy is optimized through the reinforcement learning (DQN) algorithm. The state space includes the proportion of the area of the hot spot region (>15% warning), the thickness gradient (Δh>2μm / mm), and the energy consumption constraint (<5kWh / m³). A dynamic verification system based on digital twin is designed to send the optimized parameters to the PLC controller in real time. The pulse backwashing pressure is set to 8-12MPa, and the duration is optimized to 18-25 seconds to determine the amount of pollutant deposition on the membrane surface.

[0154] The following is a specific example:

[0155] Suppose a certain desalination plant in Israel adopts the Dow SW30HRLE-400 membrane module to implement this solution. In step 501, it is determined through CFD simulation that the central region of the flow channel (width 1.2mm) is a high-vortex region, and 256 graphene sensing units are deployed (the spacing density is adjusted to 450μm), and an embedded signal acquisition module is connected to achieve parallel acquisition of 128 channels; when the influent salt concentration is detected to be 40000ppm in step 502, the Raman peak intensity drops by 18%, and the 32-64Hz characteristic frequency band is extracted by wavelet packet decomposition, and the SNR is increased to 35dB; in step 503, the LFPSO algorithm calculates that the deposition thickness in the northwest quadrant reaches 5.3μm (contact angle 68°), and XPS analysis shows that the proportion of CaCO3 is 72%; in step 504, three hot spot regions are calibrated on the heat map (total area proportion 19%), the DQN model optimizes the backwashing period to once every 4.2 hours, the pressure pulse is set to 10MPa / 22 seconds, the flux recovery rate reaches 98.5%, and the amount of pollutant deposition on the membrane surface is determined.

[0156] Steps 501-504 construct the first millimeter-level holographic monitoring system for pollution status in the field of seawater desalination through the arrangement of a hydrodynamic-guided sensing array, time-frequency joint signal analysis, fractal geometry optimization modeling, and digital twin strategy verification. Under complex influent conditions, sub-micron spatial resolution and minute-level dynamic update of the pollutant deposition thickness are achieved, significantly improving the accuracy and timeliness of the backwashing strategy, effectively solving the core problems such as the lack of blind area monitoring and large linear thickness estimation errors in traditional solutions, and providing a new technical paradigm for the intelligent maintenance and energy efficiency optimization of reverse osmosis membranes.

[0157] In order to solve the technical bottlenecks of inaccurate regional credit matching and delayed dynamic environmental response in carbon compensation transactions in seawater desalination systems, an intelligent compensation mechanism based on blockchain spatial index and renewable energy forecast linkage is proposed to break through the limitations of static quota allocation and manual review delays in traditional compensation transactions, and achieve second-level response and precise compensation for carbon emission exceeding the standard event. In some embodiments, when the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credit bound to the geographical location of the seawater desalination device is automatically matched through the smart contract in the blockchain network, and a directional carbon compensation transaction request is generated and executed according to the renewable energy credit, including:

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

[0159] In step 601, the grid binding relationship is a data structure that divides the geographic space into standard grid units so that the location of the desalination device and the renewable energy credit supplier form a fixed regional association. The emission increment threshold is the critical value of carbon emissions exceeding the standard that triggers carbon compensation transactions, which is dynamically set according to the device type and environmental capacity.

[0160] In the embodiment of the present application, the Geohash algorithm is used to encode the geographic coordinates (latitude and longitude accuracy 0.001°) into a 12-bit string (such as "sf3y9z") to establish a standard grid system of 500m×500m. The spatial database PostGIS is used to store the renewable energy credit pool data (wind power / photovoltaic quotas, historical transaction records) of each grid, and an R-tree index is constructed to accelerate geographic range queries. The on-chain smart contract preset threshold logic is deployed, and the bilinear interpolation algorithm is used to dynamically calculate the threshold curve based on the installed capacity of the device (such as 10,000m³ / d) and the environmental sensitivity coefficient (such as the coefficient of 1.2 around the mangrove reserve), and finally a grid binding relationship is generated (primary key: grid code, foreign key: credit supplier 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 the embodiments of the present application, a streaming data processing pipeline is designed to receive device sensor data (sampling interval: 1 second) in real time through Apache Kafka, and use Flink window functions to extract the average value of the real-time carbon emission increment in the recent 5 minutes. The JTS topology library is used for spatial inclusion detection. After converting the device GPS coordinates (such as longitude 46.2°E and latitude 24.5°N) into Geohash codes, a prefix match (at least the first 8 bits) is performed with the grid codes stored in the blockchain. When the verification fails, an exception handling process (such as retransmission or manual review) is triggered, and a spatial consistency proof is generated through zero-knowledge proof (zk-STARK) and written into the event log.

[0164] 603. When the real-time carbon emission increment exceeds the emission increment threshold and the verification result is inconsistent, retrieve the renewable energy credit pool according to the grid number corresponding to the geographical coordinate information, and dynamically adjust the weight of the tradable credit according to the current regional wind power generation power prediction data and photovoltaic irradiance monitoring data.

[0165] In step 603, the tradable credit weight is a priority coefficient reflecting the tradable volume of different renewable energy types under the current spatio-temporal conditions.

[0166] In the embodiments of the present application, access the API of the meteorological bureau to obtain wind power generation power prediction data (wind speed, wind direction) and photovoltaic irradiance monitoring values (W / m²), and use a random forest regression model to calculate the credit weight. The input features include: the predicted error of wind power output in the next 1 hour (RMSE), the attenuation rate of photovoltaic array efficiency (0.5 - 1.2% per day on average), and the grid connection capacity (MW). The weight distribution of the tradable credit is dynamically adjusted through a backpropagation neural network (BPNN), and the constraints are the remaining credit in the credit pool (such as wind power quota ≥ 30%) and the transaction cost threshold (≤ 0.12 USD / kWh).

[0167] 604. Generate a directional carbon compensation trading request including the carbon emission increment compensation value, the time stamp, and the grid number according to the tradable credit weight, complete the credit transfer through the atomic swap 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 indivisible protocol that ensures that blockchain transactions are either all 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 the embodiment of the present application, an atomic exchange process based on a hash time-locked contract (HTLC) is designed to generate a structured request including a compensation value (such as 5.2MWh), the timestamp (ISO 8601 format) and a grid code. The transaction data is synchronized to the energy trading alliance chain (ETC) and the carbon emission registration chain (CER) through the Polkadot cross-chain bridging technology, and a joint evidence index is constructed using the Merkle-Patricia tree. The transaction execution results are stored in IPFS shards (shard size 256KB), and a CID hash is generated as an evidence fingerprint. Finally, the credit limit transfer is completed in the Hyperledger Fabric channel, and the transaction delay is controlled within 3 seconds.

[0170] Here is a specific example:

[0171] Assume that a SWRO device (installed capacity 12000m³ / d) in Yanbu Industrial Zone, Saudi Arabia, triggers the compensation process at 14:30 on October 5, 2023. Through step 601, it is found that the device is located in Geohash grid "sf3y9z" (center point 46.18°E, 24.48°N), with a preset wind power quota of 50MWh and photovoltaic power of 80MWh, and the threshold is set to 3.2kg CO2e / h; through step 602, the real-time carbon emission increment is 3.8kg CO2e / h, extract coordinates 46.1823°E, 24.4795°N, Geohash matches the first 9 digits "sf3y9z1" and passes the verification; Step 603 predicts that the wind speed will drop by 12% in the next hour, and the photovoltaic irradiance will rise to 920W / m². Random forest calculates the weights (wind power 0.6, photovoltaic 0.9), and dynamically adjusts the tradable credit limit to 32MWh for wind power and 72MWh for photovoltaic; Step 604 generates a request {compensation amount: 4.3kg CO2e / h, timestamp: 2023-10-05T14:30:00Z, grid: sf3y9z}, completes the transfer of 3.5MWh photovoltaic credit through HTLC, and the cross-chain evidence CID is "bafy...q2vm".

[0172] Steps 601-604 build a carbon compensation system for accurate matching and instantaneous delivery of renewable energy credits through geocoding grid binding, streaming spatial verification, weather-driven weight optimization, and atomic cross-chain transactions. Under complex meteorological conditions, the dynamic adaptability and transaction reliability of credit allocation are significantly improved, and core problems such as regional matching deviation and rigid credit weight in traditional solutions are effectively solved. A full-process blockchain solution from millimeter-level coordinate positioning to megawatt-level credit transfer is provided for the desalination system, achieving second-level response and trusted closed-loop management of carbon emission exceeding the standard event.

[0173] To solve the problems of significant batch - to - batch deviations caused by process fluctuations and insufficient adaptability of traditional static mapping rules in the carbon emission quantification model during the production stage of membrane modules, a carbon footprint correction mechanism based on the dynamic coupling of sintering process - crystallinity is proposed, breaking through the limitations of the linear extrapolation model in traditional methods and achieving dynamic and precise adaptation of carbon emission quantification for membrane modules. In some embodiments, 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 batches of membrane modules through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption, including:

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

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

[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 through 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, with the input dimension including peak temperature (350 - 420 °C), pressure fluctuation during the holding stage (±0.5 MPa), and argon flow rate (10 - 25 L / min), and the output is the correlation surface between the crystallinity gradient and the process parameters. The Kolmogorov - Smirnov test is used to verify the consistency of the data distribution, and finally 32 groups of correlations between the crystallinity change gradient of the membrane material and the sintering process parameters are generated.

[0177] 702. According to the correlation, the porosity distribution characteristics, and the anti - fouling layer thickness parameters, divide the energy consumption distribution in the historical sintering process data into high - energy - consumption intervals and low - energy - consumption intervals, and establish the piece - wise mapping relationship between the membrane module type and the carbon dioxide equivalent value per unit area respectively;

[0178] In step 702, the piece - wise mapping relationship is a non - linear correspondence rule between the membrane module type and the carbon emission value per unit area divided according to different energy - consumption intervals.

[0179] In the embodiments of the present application, the fuzzy C-means clustering algorithm (FCM) is used to perform multi-modal partitioning on historical energy consumption data, and the membership threshold of 0.7 is set to determine the interval boundary. Based on the laser scanning data of the porosity of the membrane module (0.1 - 0.4 μm) and the thickness gradient of the anti-pollution layer (50 - 200 μm), a quantile regression model is constructed to establish a piecewise mapping relationship. The radial basis function neural network (RBFNN) is used to fit the non-linear relationship in the high energy consumption interval, and the multiple linear regression (MLR) is used in the low energy consumption interval. The model inputs include parameters such as the thermal efficiency of the sintering furnace (65 - 82%) and the density of the membrane layer (1.2 - 1.8 g / cm³).

[0180] 703. Based on the actual sintering energy consumption data of the production batches of the membrane module, calculate the carbon emission correction coefficient through the coupling relationship between the change gradient of the crystallinity of the membrane material and the sintering energy consumption;

[0181] In step 703, the coupling relationship is a quantitative index reflecting the interaction strength between the change gradient of crystallinity 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 the embodiments of the present application, based on the actual sintering energy consumption data (kWh / m²) and the crystallinity gradient (% / ℃), a partial least squares regression (PLSR) model is constructed to calculate the coupling strength. Monte Carlo simulation is introduced to generate 10,000 sets of process parameter combinations, and the key influencing factors are determined through Sobol index analysis (the peak temperature sensitivity accounts for 58%). An adaptive particle swarm optimization (APSO) algorithm is designed to solve the optimal carbon emission correction coefficient, and the constraint condition is the carbon emission difference threshold between adjacent batches (<5%). The output range of the carbon emission correction coefficient is set to 0.8 - 1.2, with a step size of 0.01.

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

[0184] In step 704, the production sequence 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 quantification relationship is a set of corresponding rules between the corrected membrane module type and the carbon emission value per unit area.

[0185] In the embodiments of the present application, a mapping relationship update protocol based on blockchain is designed. After binding the carbon emission correction coefficient with the production serial code (such as "DOW-20231005-L3"), it is written into the smart contract. The Merkle tree structure is used to store historical mapping versions, and each node contains a hash value, a timestamp, and a version number (v1.2.3). The compliance of the correction operation is verified through zero-knowledge proof (zk-SNARK). The updated carbon emission quantification relationship is stored in a distributed manner through IPFS (fragment size 128KB) and synchronized to all certified nodes. The version rollback mechanism ensures the traceability of the correction of carbon emission deviation and supports forward compatibility with three historical versions.

[0186] The following is a specific example:

[0187] In October 2023, a certain Saudi desalination plant implemented this solution for its production batch (serial code "SWC5-2310-B7"). In step 701, the XRD detected a crystallinity gradient of 0.28% / °C, and PCA extracted the key sintering parameters (peak temperature 395°C, holding time 2.6h), generating the correlation relationship R32 between the crystallinity change gradient of the membrane material and the sintering process parameters; in step 702, the FCM divided the energy consumption range (high energy consumption > 27.3 kWh / m²), and the RBFNN established a segmented mapping relationship (porosity 0.22μm corresponding to carbon emission 1.35 kg CO2e / m²); in step 703, the APSO calculated the carbon emission correction coefficient of 1.08 (actual energy consumption 28.1 kWh / m², crystallinity gradient 0.31% / °C), and adjusted the carbon emission value to 1.46 kg CO2e / m²; in step 704, the carbon emission correction coefficient was bound to the serial code, the mapping table of the Ethereum smart contract was updated, and the updated carbon emission quantification relationship was stored in IPFS as "bafy...q2vm", and the historical version was retained until v1.2.2.

[0188] Steps 701-704 constructed a dynamic correction system for the carbon emissions of membrane component production through multi-modal process data analysis, dynamic interval mapping modeling, coupled optimization calculation, and blockchain evidence storage and traceability. In complex process fluctuation scenarios, it significantly improved the consistency of carbon emission quantification among batches, effectively solved the benchmark value deviation problem caused by traditional static models, provided a dynamic correction paradigm driven by process parameters for the carbon management of the entire life cycle of reverse osmosis membranes, and realized accurate carbon footprint tracking from the nanoscale material properties to the production batch level.

[0189] In order to break through the technical bottleneck of fuzzy division of deposition stages and insufficient spatial resolution in the dynamic monitoring of membrane pollution in seawater desalination systems, this solution proposes a pollutant deposition quantification method based on the linkage between salt interception efficiency fluctuation and pressure gradient, solves the problems of lag in response to sudden changes in pollutant deposition rate and linearization of thickness estimation by traditional monitoring methods, and realizes millimeter-level dynamic analysis of pollutant deposition process. In some embodiments, the dynamic correlation model of membrane surface pollutant deposition is constructed based on the time domain variation trend of the salt interception efficiency fluctuation value, and the attenuation slope of the salt interception efficiency fluctuation value is converted into the pollutant deposition thickness distribution value in combination with the membrane component surface roughness parameters and the operating pressure gradient data, including:

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

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

[0192] In the embodiment of the present application, the wavelet transform is used to perform multi-scale decomposition (scale factor 0.1-10) on the salt interception efficiency fluctuation curve to extract the instantaneous change characteristics of the attenuation slope. The initial change rate (0-5 minutes) and the stable change rate (>30 minutes) are calculated by the first-order derivative, and the membrane surface roughness parameters (Ra=0.1-0.8μm) obtained by atomic force microscopy (AFM) are combined to construct a stage division model based on fractal theory (fractal dimension D=1.6-2.3). Design a dual threshold detection algorithm: when the rate change gradient>0.5% / min and the roughness increment>0.05μm / h, it is judged as a rapid accumulation stage, and the rest is a slow accumulation stage.

[0193] 802. According to the spatial distribution characteristics of the operating pressure gradient data, the surface of the membrane assembly is divided into a high pressure area and a low pressure area, and regional mapping relationships between the attenuation slope and the pollutant deposition thickness are established respectively;

[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 on 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 embodiments of the present application, based on computational fluid dynamics (CFD) simulation of the pressure field distribution on the surface of the reverse osmosis membrane (pressure range 0.5 - 8 MPa), the Kriging interpolation method is used to generate an isogram of the pressure gradient (resolution 50 μm). The membrane surface is divided into a high-pressure core area (pressure > 5 MPa) and a low-pressure edge area (pressure < 3 MPa) by k-means clustering. The exponential kernel function is used to fit the non-linear relationship in the high-pressure area, and the linear kernel function is used in the low-pressure area. The input parameters include the attenuation slope (0.2 - 3% / h), the contact angle (55 - 75°), and the flow rate (0.5 - 2 m / s), and the regional mapping relationship between the attenuation slope and the pollutant deposition thickness is output.

[0196] 803. Based on the influence of the surface roughness parameters of the membrane module on the attenuation slope, and the rapid accumulation stage and the slow accumulation stage, correct the calculated value of the deposition thickness in the regional mapping relationship to generate an initial distribution of the pollutant deposition thickness;

[0197] In step 803, the initial distribution of the pollutant deposition thickness is a basic distribution model of the deposition thickness without considering the dynamic change of pressure.

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

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

[0200] In step 804, the dynamic change combination performs a spatio-temporal coupling analysis of the attenuation rate trend changing with time and the spatial pressure gradient evolution.

[0201] In the embodiments of the present application, a spatio-temporal fusion algorithm is designed. The long short-term memory network (LSTM) is used to capture the time-domain trend of the attenuation slope (time window: 60 minutes), combined with the real-time updated data of the pressure gradient field (sampling rate: 1 Hz). Through the tensor splicing technology, the time-series features (16 dimensions) and the spatial features (64 dimensions) are fused into an 80-dimensional feature vector. The quantum particle swarm optimization (QPSO) algorithm is used to solve the optimal thickness distribution, and the fitness function includes the thickness gradient constraint (Δh < 1 μm / mm) and the energy consumption limit (backwash period > 4 hours). Finally, the pollutant deposition thickness distribution value with a confidence interval (95%) is generated.

[0202] The following is a specific example:

[0203] Suppose a Fujairah seawater desalination plant in the UAE uses Toray UTC-80 membrane modules for operation monitoring. It is detected in step 801 that the salt rejection efficiency drops by 18% in the first hour, the initial rate of 1.2% / min is extracted by wavelet analysis, and the roughness Ra increases from 0.3 μm to 0.5 μm, determining that the first 45 minutes is the rapid accumulation stage; in step 802, CFD simulation shows that the northwest quadrant is a high-pressure area (6.2 MPa), and an exponential model of the regional mapping relationship (R² = 0.92) is established to predict the deposition thickness in this area to be 5.8 μm; in step 803, SEM scanning shows that the surface pit density is 32 pits / μm², and the calculated deposition thickness value after the CNN model is corrected is adjusted to 6.3 μm (correction coefficient: 1.08); in step 804, LSTM predicts that the attenuation slope will drop to 0.4% / min in the next hour, and the QPSO optimization generates the pollutant deposition thickness distribution value, triggering the backwash pressure to increase to 9 MPa / 25 s, and the flux is restored to 97%.

[0204] Steps 801 - 804 construct a dynamic monitoring and precise prediction system for the membrane pollution deposition thickness through multi-scale time-domain analysis, pressure field spatial modeling, microscopic morphology correction, and quantum optimization algorithm. Under complex operating conditions, it significantly improves the accuracy of pollutant deposition stage division and the spatial resolution of thickness calculation, effectively solving the core problems such as time-domain response lag and pressure distribution neglect in traditional methods, providing comprehensive technical support for the intelligent maintenance of reverse osmosis membranes from second-level change capture to millimeter-level thickness analysis, and greatly improving the system operation stability and energy efficiency management level.

[0205] To solve the problems of the lack of physical-digital mapping and low cross-chain verification efficiency in the carbon compensation transaction data storage and evidence of seawater desalination systems, a storage management mechanism based on encrypted embedded identification and multi-chain collaboration is proposed, breaking through the limitations of data islands and high tampering risks in traditional storage methods, and realizing the immutability of carbon compensation transaction data and strong binding with physical devices. In some embodiments, encrypting and storing the directed carbon compensation transaction request in the blockchain distributed ledger to form a full-chain storage covering membrane material production traceability, operating carbon emission tracking, and compensation transaction verification, and writing the unique identification code of the full-chain storage into the membrane module identity recognition chip includes:

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

[0207] In step 901, the embedded membrane module identity code is a data structure that implants the unique serial number of the membrane module as metadata into the encrypted transaction data packet. The encrypted transaction data packet is a standardized data unit containing carbon compensation quantity values, timestamp sensitive information, and protected by cryptographic algorithms.

[0208] In the embodiments of the present application, the national cryptography SM4 algorithm is used to perform symmetric encryption (key length 256 bits) on the directed carbon compensation transaction request. The encryption mode selects the CBC (Cipher Block Chaining) mode and generates an initialization vector (IV). The transaction data is encapsulated in JSON-LD format, the membrane module identity code (16-bit ASCII code) is embedded at the metadata layer, and the SHA-3 hash algorithm is used to generate the data packet integrity check code. Finally, an encrypted transaction data packet (Header + Payload + MAC) that conforms to the ISO / IEC 7816 standard is generated, and the data packet size is compressed to within 512 bytes to ensure the blockchain writing efficiency.

[0209] 902. Write the encrypted transaction data packet into the blockchain distributed ledger, establish a chain association of membrane material production traceability, operating carbon emission tracking, and compensation transaction verification in the ledger to form a full-chain storage;

[0210] In step 902, the chain association is to establish an irreversible forward and backward association relationship between production, operation, and compensation data in the blockchain ledger. The full-chain storage is a complete data evidence chain covering the entire life cycle of the membrane material and verified by the blockchain.

[0211] In the embodiments of the present application, a distributed ledger architecture based on Hyperledger Fabric is designed, and three types of smart contracts are created in the channel: production traceability contract (storing the hash value of the sintering process), operation tracking contract (recording the carbon emission time series), and compensation verification contract (storing transaction vouchers). The Merkle-Patricia tree is used to construct a data association index, and the encrypted data packet is decomposed into 128-byte data blocks and written into different contracts. Cross-contract calls are used to achieve the chained association of data, forming a full-chain evidence storage.

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

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

[0214] In the embodiments of the present application, an improved PBFT (Practical Byzantine Fault Tolerance) consensus mechanism is deployed, with 4 verification nodes (Orderer) and 12 endorsement nodes (Peer) set. The distributed verification process includes: broadcasting the hash value of the encrypted data packet among nodes; verifying the data integrity through zero-knowledge proof (zk-STARK); performing smart contract condition judgment (such as the compensation amount not exceeding the preset threshold). After consensus is reached, the Keccak-256 algorithm is used to generate the unique identification code for the evidence storage "0x8b3d...a9c4_5894321_TM820D-047" (format: TXID_BlockHeight_MembraneID).

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

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

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

[0218] The following is a specific example:

[0219] Suppose the Ras Laffan desalination plant in Qatar completed a carbon offset transaction in October 2023. Through step 901, the compensation request (5.2 MWh of photovoltaic credits) is encrypted using SM4-CBC with the key "9f3a...c7b1", and the membrane module ID "UTC80-2310-B5" is embedded to generate a 512-byte encrypted transaction data packet; through step 902, the data packet is split into a production hash (CID: bafk...q2v), a running trace (timestamp 1696523400), and a compensation certificate (transaction hash 0x9e8a...f3c2) in the Fabric channel and written into three smart contracts to form a full-chain deposition; the 4 Orderer nodes obtained in step 903 reach a consensus through PBFT to generate a unique deposition identification code "0x9e8a...f3c2_5894321_UTC80-B5"; in step 904, a Trumpf TruMicro 5000 series laser is used to engrave a two-dimensional code on the chip, and a list of blockchain node verification information "Node1-4:2023-10-05T14:30:00.123Z" is stored. During the third-party audit, the full-chain deposition is read and verified through NFC.

[0220] Steps 901-904 construct a trusted deposition system for the entire life cycle of carbon offset transaction data through national cryptographic-level encryption packaging, multi-smart contract collaborative deposition, improved consensus verification, and laser micromachining physical anchoring. In a complex industrial environment, it realizes an irreversible binding between digital deposition and physical devices, effectively solving the core problems such as easy data tampering and lack of physical and digital mapping in traditional solutions, and providing a full-dimensional deposition solution for the desalination system from bit-level data security to nanometer-level physical engraving.

[0221] Figure 2 The following is a schematic structural diagram of a carbon emission accounting management system for a desalination system based on blockchain provided by the embodiments of the present application. As Figure 2 shown, the system includes:

[0222] A matching module 21 is used to integrate the carbon emission related data of the whole life cycle of the seawater desalination membrane material 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 quantitative relationship according to the membrane component type;

[0223] The acquisition module 22 is used to fix the graphene sensor array on the membrane surface of the reverse osmosis membrane assembly, collect the instantaneous change rate of the 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;

[0224] A calculation module 23, for dynamically calculating 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, combined with the operation time of the seawater desalination device and the inlet salt concentration data;

[0225] A generation module 24, which is used to automatically match the renewable energy credits bound to the geographical location of the desalination device through the smart contract in the blockchain network when the real-time carbon emission increment exceeds the preset emission increment threshold, and generate and execute a targeted carbon compensation 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 principle and technical effects of the blockchain-based seawater desalination system carbon emission accounting management method described in the illustrated embodiment will not be repeated. For the blockchain-based seawater desalination system carbon emission accounting management system in the above embodiment, the specific way in which each module and unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated 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 it. 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 carbon emission accounting management method for a seawater desalination system based on blockchain, characterized in that, include: Integrate the carbon emission-related data of the whole 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 in the production stage is matched with the corresponding carbon emission quantitative relationship according to the membrane component type; A graphene sensor array is fixed on the membrane surface of the reverse osmosis membrane assembly to collect the instantaneous change rate of the 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; According to the instantaneous change rate of the membrane flux, the amount of pollutant deposition on the membrane surface and the carbon footprint factor set, combined with the operation time of the seawater desalination device and the inlet salt concentration data, the real-time carbon emission increment caused by the degradation of membrane performance is dynamically calculated; When the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credits bound to the geographical location of the desalination device are automatically matched through the smart contract in the blockchain network, and a targeted carbon compensation transaction request is generated and executed based on the renewable energy credits; 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.

2. The method according to claim 1, characterized in that, The method dynamically calculates the real-time carbon emission increment caused by 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, combined with the operation time of the seawater desalination device and the inlet salt concentration data, including: Extracting the spatial heterogeneity distribution data of the instantaneous change rate of the membrane flux, combining 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; 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 anti-fouling layer of the membrane material 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 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.

3. The method according to claim 2, characterized in that, The method calculates 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 operation time data of the seawater desalination device, and generates the additional carbon emission correction value of membrane performance degradation, including: Extract the extreme value distribution of ambient temperature and the frequency of water inlet pressure fluctuation from the operating time data of the seawater desalination device, and combine 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; Based on the spatio-temporal distribution characteristics of the carbon emission coefficients in the transportation stage of the carbon footprint factor set, a dynamic attenuation model of the carbon emission coefficients in the transportation stage and the initial performance parameters of the anti-pollution layer is established through the correlation degree between the environmental temperature and humidity gradient in the transportation route and the hygroscopic expansion rate of the membrane material; Introduce the non-linear amplification coefficient of the crystal defect density of the membrane material in the high-temperature and high-salt environment on the degradation rate of the anti-pollution layer of the membrane material, and combine the dynamic influence factor set to calculate the spatio-temporal coupling degree between the degradation rate of the anti-pollution layer of the membrane material and the carbon emission coefficients in the transportation stage; Based on the spatio-temporal coupling degree and the salt concentration and temperature gradient data in the actual operating environment of the membrane module, a correction value of additional carbon emissions for membrane performance deterioration is generated through the product relationship between the degradation rate of the anti-pollution layer of the membrane material and the attenuation amount of the carbon emission coefficients in the transportation stage.

4. The method according to claim 1, wherein Integrate the carbon emission correlation data of the entire life cycle of the seawater desalination membrane material to form a carbon footprint factor set, where the carbon dioxide equivalent value per unit area in the production stage matches the corresponding carbon emission quantification relationship according to the membrane module type, including: Obtain the original production data of the membrane material, extract the multi-dimensional carbon emission parameters of the sintering temperature curve and chemical solvent consumption of the membrane material, and divide the multi-dimensional carbon emission parameters into discrete carbon footprint units according to the porosity distribution characteristics and anti-pollution layer thickness parameters corresponding to the membrane module type; For the production stage data in the discrete carbon footprint units, 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 batches of membrane modules through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption; Fuse the environmental temperature and humidity sensing data in the transportation stage of the membrane material and the measured data of the degradation rate in the waste stage, and expand the discrete carbon footprint units into continuous carbon footprint factors including the production, transportation, and waste stages. The continuous carbon footprint factors generate independent data index identifiers according to 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, perform service adaptability correction on the continuous carbon footprint factors to generate a carbon footprint factor set including the physical characteristic parameters of the membrane material and the environmental adaptation coefficient.

5. The method according to claim 1, characterized in that, Fix a graphene sensor array on the membrane surface of the reverse osmosis membrane module, and collect the instantaneous change rate of the membrane flux and the fluctuation value of the salt rejection efficiency in real time. Determine the deposition amount of pollutants on the membrane surface based on the fluctuation value of the salt rejection efficiency, including: Arrange the graphene sensing array in a honeycomb topological structure in a preset area on the active layer surface of the reverse osmosis membrane module, adjust the spacing density of the graphene sensing array according to the flow channel distribution characteristics of the membrane module, and physically connect the electrode leads of the graphene sensing array to the embedded signal acquisition module of the membrane module end cover; Based on the adjusted spacing density, the instantaneous change rate of membrane flux and the fluctuation value of salt retention efficiency are synchronously captured through the periodic scanning mode of the graphene sensor array, and the micro-area current signal and ion adsorption characteristic spectrum of the membrane surface 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 pollution is retained; Based on the time-domain variation trend of the salt retention efficiency fluctuation value, a dynamic correlation model of pollutant deposition on the membrane surface is constructed, and the attenuation slope of the salt retention efficiency fluctuation value is converted into a pollutant deposition thickness distribution value in combination with the membrane component surface roughness parameter and the operating pressure gradient data; According to 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.

6. The method according to claim 1, wherein When the real-time carbon emission increment exceeds the preset emission increment threshold, the renewable energy credits bound to the geographical location of the desalination device are automatically matched through the smart contract in the blockchain network, and a targeted carbon compensation transaction request is generated and executed according to the renewable energy credits, including: Preset emission increment thresholds and geographic location matching rules in the blockchain network, and 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 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 directional carbon compensation transaction request including the carbon emission incremental 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.

7. The method according to claim 4, wherein The dynamic mapping rule between the membrane component 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 component batches through the coupling relationship between the crystallinity change gradient of the membrane material and the sintering energy consumption, including: Extracting historical sintering process data of membrane component production batches, combining with the measured value of the membrane material crystallinity gradient, generating a correlation between the membrane material crystallinity gradient and the sintering process parameters; According to the association 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 component 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, calculate the carbon emission correction coefficient through the coupling relationship between the change gradient of the membrane material crystallinity and the sintering energy consumption; 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 amount between different membrane module batches.

8. The method according to claim 5, wherein Construct a dynamic correlation model of membrane surface pollutant deposition based on the time-domain change trend of the salt rejection efficiency fluctuation value. Combine the surface roughness parameters and operating pressure gradient data of the membrane module to convert the attenuation slope of the salt rejection efficiency fluctuation value into a pollutant deposition thickness distribution value, including: Extract the time-domain change data of the salt rejection efficiency fluctuation value, obtain the initial change rate and stable change rate of the attenuation slope, and combine the surface roughness parameters of the membrane module to divide the rapid accumulation stage and slow accumulation stage of pollutant deposition; According to the spatial distribution characteristics of the operating pressure gradient data, divide the surface of the membrane module into a high-pressure area and a low-pressure area, and establish the regional mapping relationship between the attenuation slope and the pollutant deposition thickness respectively; Based on the influence of the surface roughness parameters of the membrane module on the attenuation slope, and the rapid accumulation stage and slow accumulation stage, correct the calculated value of the deposition thickness in the regional mapping relationship to generate the initial distribution of the pollutant deposition thickness; Combine the time-domain change trend of the attenuation slope with the dynamic change of the operating pressure gradient data, update the initial distribution of the pollutant deposition thickness, and convert the attenuation slope into a pollutant deposition thickness distribution value.

9. The method according to claim 1, wherein Encrypt and store the directional carbon compensation trading request in the blockchain distributed ledger to form a full-chain evidence storage covering the traceability of membrane material production, operation carbon emission tracking, and compensation trading verification, and write the unique identification code of the full-chain evidence storage into the membrane module identity recognition chip, including: Encrypt the directional carbon compensation trading request to generate an encrypted trading data packet embedded with the membrane module identity code; Write the encrypted trading data packet into the blockchain distributed ledger, establish a chain association of membrane material production traceability, operation carbon emission tracking, and compensation trading verification in the ledger, and form a full-chain evidence storage; Based on the blockchain consensus mechanism, conduct distributed verification on the full-chain evidence storage to generate a unique identification code of the evidence storage including the transaction hash value; Write the unique identification code of the evidence storage into the membrane module identity recognition chip through a physical interface, and record the evidence storage writing timestamp and blockchain node verification information.

10. A carbon emission accounting management system for a seawater desalination system based on blockchain, characterized in that, Including: A matching module for integrating the carbon emission correlation data of the whole life cycle of the seawater desalination membrane material to form a carbon footprint factor set, where the carbon dioxide equivalent value per unit area in the production stage matches the corresponding carbon emission quantification relationship according to the membrane module type; A collection module for fixing a graphene sensor array on the membrane surface of the reverse osmosis membrane module, collecting the instantaneous change rate of the membrane flux and the salt rejection efficiency fluctuation value in real time, and determining the membrane surface pollutant deposition amount based on the salt rejection efficiency fluctuation value; A calculation module, for dynamically calculating the real-time carbon emission increment caused by 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, combined with the operation time of the seawater desalination device and the inlet salt concentration data; A generation module, which is used to automatically match the renewable energy credits bound 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 compensation transaction request based on the renewable energy credits; The storage module is used to encrypt and store the directed carbon compensation transaction request in a blockchain distributed ledger to form 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.

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