Intelligent purification and circulation method and system for wastewater of a recreational vehicle

Abstract

This invention discloses an intelligent purification and recycling method and system for RV wastewater, belonging to the field of wastewater treatment and reuse technology. It includes acquiring dual-mode sensor data and synchronously aligning and generating parameters; generating a matrix containing key pollution characteristics based on the parameters; executing multi-stage membrane treatment to obtain graded water quality based on matrix decisions; generating a scheduling scheme based on water quality matching reuse standards, combined with demand and environmental adjustments to prioritize; and optimizing water allocation by combining the scheme with water network status to control the efficient recycling of reclaimed water. This invention employs dual-mode sensor fusion analysis, dynamic pollution feature identification, multi-stage intelligent membrane collaborative treatment, and water network balance optimization scheduling strategies, enabling efficient deep purification, safe graded reuse, and intelligent management of RV wastewater, significantly improving water resource utilization, system operation intelligence, and user experience.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment and reuse technology, and in particular to an intelligent purification and recycling method and system for RV wastewater. Background Technology

[0002] RVs provide users with integrated mobile living and travel solutions. During daily use, they inevitably generate wastewater, primarily greywater from washing and cooking, and blackwater from toilets. Given the mobile nature of RVs and increasingly stringent environmental emission regulations, effective onboard management of this wastewater is a fundamental requirement for normal RV use. A typical RV water system usually includes a fresh water tank, greywater and blackwater collection tanks, and piping systems connecting each water point.

[0003] However, current wastewater treatment strategies commonly used in the RV industry often prove insufficient in practical applications. Many existing onboard treatment devices offer limited purification effects, and the treated water quality lacks stability, often failing to meet diverse reuse water quality standards. Therefore, users must frequently locate designated discharge points to dispose of collected wastewater, which to some extent limits the RV's continuous travel capability. Furthermore, existing systems typically lack mechanisms for tiered utilization based on treated water quality, and also lack mature solutions for effectively allocating and balancing water volumes between tanks. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an intelligent purification and recycling method and system for RV wastewater. Employing dual-mode sensor fusion analysis, dynamic pollution feature identification, multi-level membrane intelligent collaborative treatment, and water network balance optimization scheduling strategy, it can achieve efficient and deep purification and safe graded reuse of RV wastewater, significantly improving water resource utilization and the level of system operation intelligence.

[0005] The above objectives can be achieved through the following approach:

[0006] A method for intelligent purification and recycling of RV wastewater includes: acquiring dual-mode detection signals of wastewater and synchronizing and aligning the signals to generate optical and electrochemical parameter data; analyzing and modeling the optical and electrochemical parameter data to generate a pollution feature matrix, which includes spectral attenuation rate, organic oxidation degree, and particle size distribution characteristics; deciding and executing a multi-level membrane treatment instruction set based on the pollution feature matrix to obtain a graded purification water quality parameter set, wherein the multi-level membrane treatment instruction set sets membrane module switching thresholds and catalytic treatment parameters; analyzing available reuse pathways based on the graded purification water quality parameter set and matching it with a preset reuse standard set, and adjusting the priority order of the available reuse pathways in conjunction with pre-collected dynamic environmental parameters to generate a circulating water scheduling path allocation scheme; generating a water volume allocation instruction for dynamic balance optimization of the water network based on the circulating water scheduling path allocation scheme and in conjunction with real-time acquired water network status information and water storage demand, and executing the circulating water scheduling path allocation scheme and the water volume allocation instruction to control the graded transportation and recycling of reclaimed water.

[0007] Optionally, the generation of optical and electrochemical parameter data includes: acquiring reflectance spectral data with a continuous wavelength distribution through an optical sensor array; measuring charge transfer impedance data of organic matter in wastewater through a bioelectrochemical sensor; and aligning the reflectance spectral data and the charge transfer impedance data along the time axis to generate synchronously time-aligned optical and electrochemical parameter data.

[0008] Optionally, generating the pollution feature matrix includes: decomposing the reflectance spectral data into scattering and absorption components to determine particle size distribution characteristics and spectral attenuation rate; performing wavelet noise reduction on the charge transfer impedance data and matching it with a pollutant feature library to determine the organic oxidation degree index; constructing a three-dimensional data space using the spectral attenuation rate, the organic oxidation degree index, and the particle size distribution characteristics; and calculating pollution weight factors using a convolutional neural network based on the three-dimensional data space to generate a dynamically updated pollution feature matrix.

[0009] Optionally, the step of deciding and executing a multi-level membrane treatment instruction set based on the pollution feature matrix includes: determining the catalytic treatment parameters based on the organic oxidation degree index; determining the membrane module switching threshold and related operating parameters based on the particle size distribution characteristics and the chemical residue characteristics in the pollution feature matrix; and combining the catalytic treatment parameters, the membrane module switching threshold, and the related operating parameters to generate a multi-level membrane treatment instruction set.

[0010] Optionally, the step of making decisions and executing a multi-level membrane treatment instruction set based on the pollution feature matrix further includes: matching and selecting a reagent formula from a preset atomizing reagent formula library based on the chemical residue characteristics reflected in the pollution feature matrix; dispersing the reagent formula into micron-sized particles and injecting it into the water to be treated by magnetically driving the atomizing nozzle; and dynamically correcting the contact reaction time between the reagent formula and the wastewater by utilizing the real-time change rate of the online oxidation-reduction potential monitoring value.

[0011] Optionally, the method for generating a circulating water scheduling path allocation scheme includes: mapping the graded purified water quality parameter set to preset Class I, Class II, and Class III water quality levels; determining the available reuse paths based on the water quality levels and the preset path mapping relationship between each level and the reuse system; and adjusting the priority sequence according to the available reuse paths and the real-time water demand of the vehicle to generate a circulating water scheduling path allocation scheme.

[0012] Optionally, adjusting the priority sequence based on the available reuse paths and the real-time water demand of the vehicle includes: increasing the priority of the greening irrigation path when the external ambient temperature exceeds a preset high temperature threshold; increasing the priority of the drinking water replenishment path when the remaining capacity of the clean water tank is lower than a preset safety threshold; and assigning the first priority to the bathroom system water supply path when the vehicle is parked and there is a demand for bathroom water.

[0013] Optionally, generating the water allocation instruction for the dynamic balance optimization of the water network includes: real-time acquisition of a system state matrix containing the capacity of each water tank, pipeline pressure, and pump energy consumption; based on the system state matrix, optimizing the water exchange sequence and value between adjacent water tanks using a preset reinforcement learning model to obtain a basic water exchange strategy; predicting the water consumption fluctuation trend in future periods based on historical water consumption data in a preset historical database to generate a pre-regulation control instruction; and fusing the basic water exchange strategy with the pre-regulation control instruction to generate the water allocation instruction for the dynamic balance optimization of the water network.

[0014] Optionally, the allocation scheme based on the circulating water scheduling path further includes: continuously monitoring the graded purified water quality parameters; when any monitored graded purified water quality parameter exceeds a preset safety range, generating an isolation command signal; based on the isolation command signal, performing isolation and import treatment of abnormal water bodies, the isolation and import treatment of abnormal water bodies includes cutting off the original water supply path, activating a backup treatment channel, and importing the abnormal water body into a secondary treatment chamber; and based on the isolation command signal, recording abnormal characteristic parameters and reversing the weight allocation of the pollution characteristic matrix.

[0015] Based on the same inventive concept, this invention also provides an intelligent purification and recycling system for RV wastewater. The system includes: a dual-mode sensing and data fusion module, configured with an optical sensor array and a bioelectrochemical sensor, for acquiring dual-mode detection signals of wastewater and performing synchronization processing to generate optical and electrochemical parameter data; a pollution feature modeling module, for receiving the optical and electrochemical parameter data and generating a dynamically updated pollution feature matrix through analysis and modeling; an intelligent purification control module, configured with multi-stage membrane treatment units and a controller, for generating and executing a set of multi-stage membrane treatment instructions based on the pollution feature matrix to obtain a graded purification water quality parameter set; a reuse pathway planning module, for performing water quality assessment, path analysis, and priority adjustment based on the graded purification water quality parameter set and related information to generate a circulating water scheduling path allocation scheme; and a smart water network execution and protection module, connecting water network system sensors and actuators, for performing dynamic balance optimization of the water network based on the circulating water scheduling path allocation scheme to generate water allocation instructions, and combining real-time water quality safety monitoring results to achieve controlled, safe recycling of reclaimed water and abnormal response handling.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] 1. Improved accuracy and real-time performance of wastewater diagnosis. By integrating optical and electrochemical dual-mode sensing information and using intelligent algorithms to generate a dynamic pollution feature matrix, this invention can comprehensively and in real-time grasp the characteristics of wastewater quality, overcoming the shortcomings of traditional single detection methods that provide limited information, and providing a reliable basis for subsequent precise treatment;

[0018] 2. Enhanced efficiency and adaptability of purification treatment. Based on real-time pollution characteristics, this invention can intelligently regulate the operating parameters of multi-stage membrane treatment units and accurately compensate for chemical dosing, achieving dynamic matching between the purification process and wastewater quality. Compared with fixed parameters or simple logic control, it can treat complex and variable RV wastewater more efficiently and stably.

[0019] 3. The efficiency and management level of reclaimed water recycling have been optimized. This invention not only scientifically classifies purified water, but also establishes an intelligent mapping between water quality and reuse pathways. It also dynamically adjusts water delivery priority and optimizes water tank allocation based on environmental and demand factors, thereby maximizing and refining the utilization of reclaimed water resources, which is significantly better than the traditional extensive reuse or discharge mode.

[0020] 4. Improved system reliability, security, and intelligence. The integrated rapid detection, isolation, and reprocessing mechanism for water quality anomalies effectively ensures water safety, while the dynamic water network balance algorithm based on reinforcement learning and prediction enhances the system's self-optimization and stable operation capabilities, making the entire water system more intelligent, reliable, and efficient.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of an intelligent purification and recycling method for RV wastewater according to an embodiment of the present invention.

[0024] Figure 2 This is a comparison chart of the graded purification water quality parameter set in an embodiment of the present invention.

[0025] Figure 3 This is a dynamic change diagram of the water tank capacity according to an embodiment of the present invention.

[0026] Figure 4 This is a heat map of the pollution feature matrix according to an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram of the structure of an intelligent purification and recycling system for RV wastewater according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 One embodiment of the present invention proposes an intelligent purification and recycling method for RV wastewater. It adopts optical and electrochemical dual-mode sensing fusion analysis, dynamic pollution feature identification, multi-stage membrane treatment intelligent control, and water network balance optimization scheduling strategy, which can realize efficient deep purification, safe graded reuse, and intelligent management of RV wastewater, significantly improving the water resource utilization rate and user experience of RVs.

[0030] The method described in this embodiment specifically includes:

[0031] Acquire wastewater dual-mode detection signals and synchronize and align the wastewater dual-mode detection signals to generate optical and electrochemical parameter data;

[0032] Specifically, dual-mode wastewater detection signals refer to raw wastewater measurement signals acquired using sensing technologies based on at least two different physical or chemical principles, such as optical and electrochemical methods. Synchronous time alignment is the process of unifying these signals from different sources, potentially with different sampling frequencies, on a time reference. Common methods include timestamp alignment or data resampling based on interpolation algorithms to ensure the accuracy of subsequent data fusion analysis. The resulting optical and electrochemical parameter data is a structured dataset containing multi-dimensional information reflecting the current state of the wastewater, serving as the basis for subsequent analysis and modeling.

[0033] Based on the optical and electrochemical parameter data, analysis and modeling are performed to generate a pollution feature matrix, which includes spectral attenuation rate, organic oxidation degree and particle size distribution characteristics.

[0034] Specifically, this step aims to extract key information from the parameter data obtained in the previous step to construct a mathematical model that can quantitatively characterize the main pollution status of wastewater, namely, a pollution feature matrix. This matrix is ​​a dynamically updated data structure, whose core elements include: spectral attenuation rate, which reflects the attenuation of light as it propagates in wastewater and is related to color and dissolved organic matter concentration; organic oxidation degree, which measures the total amount of organic pollutants in wastewater or the ease with which they can be oxidized; and particle size distribution characteristics, which describe the size and distribution of suspended particulate matter in the wastewater. The quantified values ​​or relative weights of these features constitute the pollution feature matrix, providing a basis for subsequent purification treatment decisions.

[0035] Based on the pollution feature matrix, a multi-level membrane treatment instruction set is executed to obtain a graded purification water quality parameter set. The multi-level membrane treatment instruction set sets the membrane module switching threshold and catalytic treatment parameters.

[0036] Specifically, based on the real-time updated pollution feature matrix, an adaptive multi-stage membrane treatment instruction set is generated through an intelligent decision-making mechanism, such as using a rule engine, fuzzy logic, or a pre-trained machine learning model. The multi-stage membrane treatment typically refers to the combined use of different levels of membrane separation technologies, such as ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO), and may be supplemented by advanced oxidation processes such as photocatalytic oxidation (PCM). The instruction set mainly includes setting the operating parameters of each treatment unit, specifically catalytic treatment parameters, such as the excitation intensity or reaction residence time of the ultraviolet (UV) lamp in the PCM reactor; as well as membrane module switching thresholds and related operating parameters, and setting their operating pressure or backwashing frequency. The controller executes the instruction set, driving the corresponding physical treatment units to operate, and monitors the purification effect in real time through online water quality sensors, ultimately obtaining a set of graded purified water quality parameters. These parameters may include key indicators such as turbidity, total organic carbon (TOC), and conductivity. Comparison of water quality parameter sets for graded purification at different water quality levels, such as... Figure 2 As shown.

[0037] Based on the analysis of available reuse pathways by matching the set of graded purified water quality parameters with the preset reuse standard set, and by combining the pre-collected dynamic environmental parameters, the priority order of the available reuse pathways is adjusted to generate a circulating water scheduling path allocation scheme.

[0038] Specifically, the acquired set of graded purified water quality parameters is matched with a stored set of preset reuse standards. This set of standards defines the specific water quality requirements for different water usage scenarios within the RV, such as washing, toilet flushing, and green space irrigation. Through matching analysis, it is determined which standards the current purified water meets, thereby deriving a set of available reuse pathways. Next, dynamic environmental parameters are collected. These parameters may include the outside ambient temperature, the current remaining capacity of the clean water tank, and the vehicle's operating status (parked or moving). Combined with real-time water demand signals from users, such as trigger signals from faucets or toilets, the priority order of all currently available reuse pathways is adjusted in real time using preset priority rules or algorithms. Finally, a dynamic, priority-based circulating water scheduling path allocation scheme is generated, providing decision-making guidance for subsequent water allocation and delivery.

[0039] Based on the circulating water scheduling path allocation scheme, combined with the real-time acquired water network status information and water storage demand, a water allocation instruction for dynamic balance optimization of the water network is generated, and the circulating water scheduling path allocation scheme and the water allocation instruction are executed to control the graded transportation and recycling of reclaimed water.

[0040] Specifically, this is the final control and execution stage. The control core, based on the circulating water scheduling path allocation scheme generated in the previous step, clarifies the flow direction and priority of the purified water. Simultaneously, it acquires real-time water network status information through a sensor network. This information includes the current liquid level or capacity of each water tank in the RV, such as the grey water tank, black water tank, various levels of purified water tanks, and clean water tank, as well as the pressure at key nodes in the connecting pipelines and the real-time energy consumption of each water pump. Combining this status information with the user's water storage needs, such as prioritizing the clean water tank's water volume or meeting the needs of specific water usage points, a dynamic water network balance optimization algorithm is run. This algorithm aims to generate optimal water allocation instructions. These instructions not only include water delivery instructions to meet the highest priority path in the scheduling scheme but can also include optimization instructions for transferring water between tanks to achieve overall water level balance, reduce energy consumption, or prepare for subsequent water use. Ultimately, the controller executes the circulating water scheduling path allocation scheme and the generated water volume allocation command. By precisely controlling the start / stop and speed of the relevant water pumps, as well as the opening / closing status of the pipeline solenoid valves, it achieves graded delivery and recycling of reclaimed water according to the planned grade, path, and optimized water volume. The water tank capacity changes during the dynamic balance optimization process of the RV water network, such as... Figure 3 As shown.

[0041] This invention achieves closed-loop intelligent management of RV wastewater from generation to recycling through integrated sensing, intelligent analysis and decision-making, adaptive processing control, and globally optimized scheduling management. This not only improves the recycling rate of water resources and reduces the environmental impact of wastewater discharge, but also greatly enhances the water convenience for RV users and the endurance of long-distance travel.

[0042] Optionally, the generation of optical and electrochemical parameter data includes:

[0043] Reflectance spectrum data with continuously distributed wavelengths are acquired through an optical sensor array;

[0044] Specifically, the process involves acquiring continuously distributed wavelength reflectance spectral data using an optical sensor array (OSA). This OSA typically consists of one or more multi-wavelength light sources or tunable light sources and a matching spectral detector. The light source emits light covering a specific wavelength range towards the wastewater area to be treated. When the light shines on the wastewater surface or passes through the wastewater, some of the light is absorbed by various substances in the wastewater. The reflectance spectral data is a record of the change in the intensity of the light signal reflected from the wastewater surface or interior over time at different wavelengths. This spectral data contains information reflecting the wastewater's color, turbidity, and the absorption or scattering characteristics of certain chemical components at specific wavelengths. For example, the turbidity T of the wastewater can be correlated with the absorption or scattering characteristics of certain chemical components at a specific wavelength λ. turb Scattered light intensity at the location The relationship is approximately linear or nonlinear, while the concentration C of certain organic compounds... org Possibly related to a specific ultraviolet wavelength λ uv Absorbed light intensity at Related. The collected raw reflectance spectral data is usually represented as a high-dimensional time series, where each time point corresponds to a vector containing multiple wavelength light intensity values.

[0045] Charge transfer impedance data of organic matter in wastewater were measured using a bioelectrochemical sensor.

[0046] Specifically, the bioelectrochemical sensor typically includes a working electrode, a counter electrode, and a reference electrode. A bioactive material sensitive to specific organic compounds, such as a microbial community or purified enzyme, is immobilized on the surface of the working electrode. When wastewater flows through the sensor, the organic matter in the wastewater undergoes a biocatalytic reaction under the action of the bioactive material, accompanied by electron transfer at the electrode interface. Electrochemical impedance spectroscopy (EIS) is used to detect the kinetics of this charge transfer process. By applying a small-amplitude alternating current (AC) potential signal superimposed on a direct current (DC) bias to the sensor and measuring the resulting AC current response, a series of impedance values ​​at different AC frequencies can be obtained. Representing these impedance data in a complex plane or logarithmic coordinate system, and fitting an equivalent circuit model, the charge transfer impedance (Rct) value can be extracted. The level of the charge transfer impedance is related to the reaction rate of organic matter on the electrode surface, and thus can indirectly reflect the concentration or biochemical activity of electrochemically reactive organic matter in the wastewater. The equivalent circuit model can be a complex distributed circuit model or a simplified model. For a simplified Randle circuit, the total impedance Z(ω) varies with the angular frequency ω as follows:

[0047]

[0048] Where Z(ω) is the total impedance at angular frequency ω, and R s It is the resistance of the solution, R ct It is the charge transfer impedance, j is the imaginary unit, ω is the angular frequency of the AC signal, equal to 2πf, where f is the frequency, and C dl It is a double-layer capacitor, α is the diffusion index of a constant phase element (CPE) used to describe the behavior of a non-ideal capacitor, and W(ω) is the impedance, which describes the diffusion process on the electrode surface.

[0049] The reflection spectrum data and the charge transfer impedance data are aligned on the time axis to generate synchronously time-aligned optical and electrochemical parameter data.

[0050] Specifically, aligning the reflectance spectral data with the charge transfer impedance data along the time axis is a crucial preprocessing step before data fusion, aiming to map data from different sources onto a unified time reference. This is typically achieved by appending a precise timestamp to each acquired data point. Then, a common time resolution or sampling rate is chosen as the alignment target, such as once per second.

[0051] Optionally, the generation of the pollution feature matrix includes:

[0052] The reflectance spectral data is decomposed into scattering and absorption components to determine the particle size distribution characteristics and spectral attenuation rate.

[0053] Specifically, the reflectance spectral data is decomposed into a scattering component (SC) and an absorption component (AC), and based on this, the particle size distribution (PSD) of suspended particles in the wastewater and the spectral attenuation rate (SAR) of the liquid are determined. When a broadband beam of light irradiates wastewater, the loss of light energy mainly stems from scattering by suspended particles in the wastewater and absorption by dissolved substances. The reflectance spectral data contains composite information about these interactions. To separate and quantify these effects, spectral signal processing techniques can be applied. For example, by establishing a radiative transfer model describing the transmission of light in scattering and absorption media, or by employing multivariate decomposition algorithms such as non-negative matrix factorization (NMF), the measured reflectance spectral data can be resolved into two independent spectral curves representing the scattering contribution and the absorption contribution, respectively. The shape and intensity of the scattering component spectral curve are closely related to the concentration, size, shape, and optical properties of suspended particles in the wastewater. Based on the scattering components and combined with optical models such as Mie scattering theory, the particle size distribution characteristics of suspended particulate matter in wastewater can be deduced. For example, for particles assumed to be spherical, the scattering cross section σ caused by a single particle with diameter d at a specific wavelength λ can be calculated. scat (λ,d) is the core output of the Mie scattering theory, while the total scattering intensity I scatter (λ) is the superposition of the scattering effects of all particles of different sizes in the system. In a set of particles, the total scattering intensity can be expressed as the integral of the particle size distribution function N(d) over the scattering cross section:

[0054]

[0055] Among them, I scatter (λ) represents the total scattering intensity at wavelength λ. This represents the particle diameter d at the minimum diameter d. min To the maximum diameter d max The integral over the range, N(d) represents the number concentration of particles with diameter d in the wastewater, σ scat(λ,d) represents the scattering cross section of a particle with diameter d at wavelength λ, and dd represents the integral with respect to d. By solving or fitting the above equation, we can obtain I scatter (λ) Estimate the particle size distribution function N(d) or its statistical parameters, such as average particle size and particle size range; these are the particle size distribution characteristics. The absorption component spectral curve is mainly related to the absorption characteristics of dissolved substances in wastewater, and usually shows absorption peaks within a specific wavelength range. Based on the absorption component, the spectral attenuation rate (SAR) of the liquid can be calculated, which comprehensively reflects the degree of attenuation caused by absorption and scattering of light in wastewater.

[0056] The charge transfer impedance data is subjected to wavelet noise reduction and matched with a pollutant feature library to determine the organic oxidation degree index.

[0057] Specifically, the charge transfer impedance data is subjected to wavelet denoising. Bioelectrochemical sensors are susceptible to interference from various factors during data acquisition, such as power fluctuations, environmental noise, and instantaneous changes in biofilm activity. These interferences introduce noise, affecting the accuracy of the charge transfer impedance data. Wavelet denoising is an effective signal denoising method. It decomposes the original signal at different frequency scales, identifies the high-frequency components where noise is present, performs thresholding on these components, and finally reconstructs a smooth signal that retains the main features through inverse transform. The denoised charge transfer impedance data can more accurately reflect the biochemical reactions or electrochemical activity of organic matter in wastewater at the sensor electrode interface. Subsequently, the denoised charge transfer impedance data is matched with a preset pollutant feature library to determine the Organic Oxidation Degree Index (OODI). The pollutant feature library is a database storing standard electrochemical impedance spectra or extracted feature values ​​corresponding to various typical RV wastewater pollutants. The matching process involves comparing the current denoised charge transfer impedance data or its extracted features with the standard features stored in the library, calculating their similarity or distance. For example, the Euclidean distance or correlation coefficient between the feature vector of the current data and the feature vector of each entry in the library can be calculated to find the closest match. Based on the matched library entries and their corresponding known information, a quantified organic oxidation degree index is determined. This index aims to reflect the content, structural complexity, or ease of oxidation of total organic matter in wastewater. For example, matching easily degradable organic matter characteristics with low impedance results in a higher organic oxidation degree index score.

[0058] A three-dimensional data space is constructed using the spectral attenuation rate, the organic oxidation degree index, and the particle size distribution characteristics.

[0059] Specifically, a three-dimensional data space is constructed using the spectral attenuation rate, the organic oxidation index, and a representative quantified value of the particle size distribution characteristic determined through the aforementioned steps. At each consecutive sampling or analysis time point, these three quantified characteristic values ​​are used as values ​​on the coordinate axes to form a three-dimensional feature point. For example, one time point corresponds to one three-dimensional feature vector, whose components are the spectral attenuation rate calculated at that time, the determined organic oxidation index value, and the representative particle size characteristic value. Over time, these continuous three-dimensional feature points together constitute a feature space that dynamically changes over time, i.e., the three-dimensional data space. This space intuitively represents the comprehensive pollution state of wastewater in optical and electrochemical dimensions, providing standardized multi-dimensional input for subsequent more complex machine learning model analysis.

[0060] Based on the aforementioned three-dimensional data space, a convolutional neural network is used to calculate pollution weight factors and generate a dynamically updated pollution feature matrix.

[0061] Specifically, based on the data stream in the three-dimensional data space, a Convolutional Neural Network (CNN) is used to calculate the Pollution Weight Factor (PWF) and generate a dynamically updated Pollution Feature Matrix (PFM). A sequence of continuously sampled three-dimensional feature vectors within a time window is fed into a pre-trained CNN model. This CNN model is designed to identify complex patterns and correlations in the three-dimensional feature space related to specific wastewater pollution types or degrees. The pollution weight factor is a vector, where each element represents the confidence or probability that the wastewater belongs to a preset pollution category or is at a certain pollution severity level. These weight factors together constitute the dynamically updated Pollution Feature Matrix (PFM). The CNN model is trained on a large dataset of wastewater samples with known pollution types and degrees. By adjusting the model parameters, it can accurately map the input three-dimensional feature space patterns to the correct pollution weight factor outputs.

[0062] Optionally, the step of deciding and executing a multi-level membrane treatment instruction set based on the pollution feature matrix includes:

[0063] The catalytic treatment parameters are determined based on the organic matter oxidation degree index.

[0064] Specifically, the catalytic treatment parameters are determined based on the organic matter oxidation degree index. The organic matter oxidation degree index reflects the relative content of total organic matter in wastewater and its susceptibility to oxidation. Catalytic treatment typically refers to advanced oxidation processes, which require adjustments to operating parameters based on the characteristics of the organic matter to be treated to achieve optimal treatment results and energy efficiency.

[0065] Based on the particle size distribution characteristics and the chemical residue characteristics in the fouling characteristic matrix, the switching threshold and related operating parameters of the membrane module are determined.

[0066] Specifically, the characteristics of chemical residues in the fouling feature matrix, such as the presence and weighting factors of surfactants, heavy metal ions, or specific organic chemicals, affect the precision of membrane selection, potential membrane fouling risk, and the required transmembrane pressure or backwashing strategy. This step assesses the current particle size distribution characteristics, such as average particle size or particle size range, and combines this with the types and concentrations of chemical residues reflected in the fouling feature matrix. Using a pre-defined decision logic or lookup table, it determines the type of membrane module to be used and its switching threshold. Simultaneously, it sets the relevant operating parameters for the membrane module, including the frequency and intensity of backwashing or chemical cleaning, and the feed water flow rate, to maximize membrane flux, extend its lifespan, and ensure purification effectiveness.

[0067] The catalytic treatment parameters, the membrane module switching threshold, and the relevant operating parameters are combined to generate a multi-level membrane treatment instruction set.

[0068] Specifically, the catalytic treatment parameters, membrane module switching thresholds, and related operating parameters are combined to generate the multi-stage membrane treatment instruction set. This instruction set is a structured data package containing specific operating instructions for each membrane treatment unit and catalytic treatment unit in the control system for the current or future period. These instructions include, but are not limited to, starting or stopping a membrane module, setting a target operating pressure value for a specific membrane module, setting the UV lamp power or operating time of the catalytic reactor, setting the trigger conditions or cycle for backwashing or chemical cleaning, and adjusting the flow rate of the feed pump. The instruction set is dynamically generated based on a real-time fouling feature matrix, aiming to enable the multi-stage membrane treatment units and catalytic treatment units to work collaboratively to optimize and effectively remove pollutants from the current wastewater, achieving the expected graded purification water quality target. The fouling feature matrix heatmap, such as... Figure 4 As shown.

[0069] Optionally, the step of deciding and executing a multi-level membrane treatment instruction set based on the pollution feature matrix further includes:

[0070] Based on the characteristics of chemical residues reflected in the pollution feature matrix, a reagent formula is matched and selected from a preset atomizing agent formula library;

[0071] Specifically, this is an auxiliary chemical treatment step performed when necessary. Based on the characteristics of chemical residues reflected in the pollution feature matrix, a reagent formulation is matched and selected from a preset atomized reagent formulation library. The pollution feature matrix is ​​evaluated using a machine learning model, which quantifies the weighting factors of specific recalcitrant chemicals or components with potential membrane fouling risks present in the wastewater. When these weighting factors exceed preset thresholds, it is determined that additional chemical treatment is needed to degrade or modify these substances. The atomized reagent formulation library stores various optimized reagent formulations for common chemical residues in RVs, such as trace formulations based on hydrogen peroxide, ozone, or specific catalysts. Based on the specific chemical residue type and concentration indicated in the pollution feature matrix, the most suitable reagent formulation for treating the current pollutant is searched and selected from this library.

[0072] The reagent formulation is dispersed into micron-sized particles and injected into the water to be treated by a magnetically driven atomizing nozzle. The contact reaction time between the reagent formulation and the wastewater is dynamically adjusted by using the real-time change rate of the online oxidation-reduction potential monitoring value.

[0073] Specifically, this step describes the dosing method and reaction control of the selected reagent. The reagent formulation is dispersed into micron-sized particles and injected into the water to be treated via a magnetically driven atomizing nozzle. The magnetically driven atomizing nozzle utilizes high-frequency vibration or centrifugal force to break the liquid reagent into tiny droplets on the micron scale, significantly increasing the contact surface area between the reagent and the wastewater, thus improving reaction efficiency and reagent utilization. The reagent is directly sprayed or injected into the wastewater stream in atomized form. Simultaneously, the contact reaction time between the reagent formulation and the wastewater is dynamically adjusted using the real-time change rate of the online oxidation-reduction potential (ORP) monitoring value. Oxidation-reduction potential is an indicator of the strength of oxidizing or reducing properties of a solution; chemical oxidation reactions cause changes in the ORP value. By monitoring the rate of change of the ORP value in real time, the reaction progress between the reagent and the organic matter can be determined. When the rate of change of the ORP value decreases or tends to stabilize, it indicates that the reaction is nearing completion. Based on this, the residence time of wastewater in the reaction chamber or the timing of the start-up of subsequent treatment can be dynamically adjusted to ensure that the reagents react fully while avoiding over-dosing.

[0074] Optionally, the generated circulating water scheduling path allocation scheme includes:

[0075] The graded water quality parameter set is mapped to preset Class I, Class II, and Class III water quality levels;

[0076] Specifically, this step involves a standardized assessment of the purified water quality. The set of graded purified water quality parameters is mapped to preset Class I, Class II, and Class III water quality levels. In a motorhome water recycling system, different water quality requirements exist depending on the reuse pathway, such as drinking, washing, toilet flushing, or greening irrigation. The preset Class I, Class II, and Class III water quality levels correspond to the required purification levels for these different reuse pathways, with Class I being the highest and Class III the lowest. The set of graded purified water quality parameters, such as turbidity, total organic carbon, conductivity, and microbial count, obtained from the multi-stage membrane treatment unit is compared with the preset water quality standards defining these three levels to determine which water quality level the current purified water belongs to.

[0077] Based on the water quality grades and the preset path mapping relationship between each grade and the reuse system, the available reuse pathways are determined;

[0078] Specifically, this step determines feasible reuse directions based on water quality assessment results. The available reuse pathways are determined based on the water quality level and the preset path mapping relationship between each level and the reuse system. An internal mapping table or rule set is stored, specifying which specific water use scenarios or reuse systems within the RV can safely utilize each water quality level. For example, Class I water quality can be mapped to all reuse pathways, Class II water quality to washing and toilet flushing, and Class III water quality only to toilet flushing and landscaping. Based on the current purified water quality level determined in the previous step, this mapping relationship is queried to determine the set of all available reuse pathways to which this batch of purified water can safely flow at the current moment.

[0079] Based on the available reuse pathways and the real-time water demand of the vehicle, the priority sequence is adjusted to generate a circulating water scheduling path allocation scheme.

[0080] Specifically, this step involves making the final decision on reclaimed water allocation. Based on the available reuse pathways and the real-time water demand of vehicles, a priority sequence is adjusted to generate a circulating water scheduling path allocation scheme. While identifying available paths is crucial, not all available paths have equally important or urgent needs. All available reuse pathways, users' real-time water demand, and vehicle environmental parameters are comprehensively considered. Using preset priority rules or algorithms, such as prioritizing drinking water needs, followed by washing, then toilet flushing, and finally landscaping, all available paths are prioritized in real-time. The final generated circulating water scheduling path allocation scheme is an ordered list of reuse paths, clearly defining which water quality levels should be prioritized for delivery to which water usage points, and the corresponding water volume allocation targets, thus guiding the actual operation of the water network.

[0081] For example, the purification system produces Class II water. According to the mapping relationship, Class II water can be used for washing and flushing the toilet. When a user presses the toilet flush button, a flushing demand is detected. Based on priority rules, a scheduling plan is generated, allocating this batch of Class II water to the toilet tank with the highest priority.

[0082] Optionally, adjusting the priority sequence based on the available reuse pathways and the real-time water demand of the vehicle includes:

[0083] If the external ambient temperature is detected to exceed the preset high temperature threshold, the priority of the greening irrigation path will be increased.

[0084] Specifically, when the detected external ambient temperature exceeds a preset high-temperature threshold, the priority of the greening irrigation path is increased. The RV's external temperature sensor continuously monitors the ambient temperature. When the detected external temperature exceeds the preset high-temperature threshold, such as 30 degrees Celsius, it is determined that there is a need for greening irrigation, or that reclaimed water is needed to assist in cooling external heat dissipation equipment. In this case, even if there is no direct signal to trigger greening irrigation, the priority of the available greening irrigation path will be appropriately increased in the circulating water scheduling scheme to prepare for water demand or auxiliary heat dissipation.

[0085] If the remaining capacity of the clean water tank is detected to be lower than the preset safety threshold, the priority of the drinking water replenishment route will be increased.

[0086] Specifically, when the remaining capacity of the fresh water tank is detected to be below a preset safety threshold, the priority of the drinking water replenishment path is increased. The fresh water tank level sensor monitors the water level in the RV's fresh water tank in real time. The fresh water tank is the main source of drinking and domestic water for the RV. When its remaining capacity drops below the preset safety threshold, for example, below 20% of the total capacity, it indicates a shortage of fresh water resources. At this time, if reclaimed water meeting Class I water quality standards can be produced, the priority of the drinking water replenishment path will be immediately increased to the highest level, ensuring that as soon as qualified reclaimed water is produced, it is prioritized to replenish the fresh water tank, guaranteeing the user's drinking water safety.

[0087] When a vehicle is detected to be parked and has a need for bathroom water, it is assigned the highest priority to the bathroom system's water supply path.

[0088] Specifically, when the vehicle is detected to be parked and there is a need for bathroom water, the highest priority is assigned to the bathroom system water supply path. The RV's position and motion sensors can determine whether the vehicle is in motion or parked. Bathroom water use typically occurs when the vehicle is parked. When the vehicle is detected to be parked, and a water use trigger signal is received from the bathroom system, and it is determined that reclaimed water meeting bathroom water standards is available, the available path for the bathroom system water supply will be immediately assigned the highest priority, ensuring that the user can obtain reclaimed water that meets their needs in a timely manner.

[0089] For example, the RV is parked in the wild, the outside temperature has risen to 32 degrees Celsius, and the fresh water tank capacity has dropped to 15%. The purification system has just produced Class II water suitable for washing. According to priority rules, high temperature triggers a moderately higher priority for greening irrigation, while low fresh water tank capacity triggers the highest priority for drinking water replenishment. The user then turns on the sink faucet. Determining that the vehicle is parked, there is a need for sanitation, and Class II water is available, the sanitation route is set to the highest priority, and Class II water is immediately delivered to the sink.

[0090] Optionally, the water allocation instruction for generating the dynamic balance optimization of the water network includes:

[0091] Real-time acquisition of a system status matrix including the capacity of each water tank, pipeline pressure, and pump energy consumption;

[0092] Specifically, the real-time data acquisition includes a System State Matrix (SSM) of the water tank capacity, pipeline pressure, and pump energy consumption. This SSM is a real-time updated data structure acquired through sensors deployed throughout the RV's water network. These sensors include capacity sensors that measure the current liquid level in each tank, pressure sensors that monitor water pressure in key pipe sections, and energy consumption sensors that monitor the real-time power consumption of each water pump. The SSM integrates this heterogeneous sensor data to comprehensively and in real-time reflect the current physical operating status and resource distribution of the RV's water circulation system.

[0093] Based on the system state matrix, a preset reinforcement learning model is used to optimize the water exchange sequence and value between adjacent water tanks to obtain a basic water exchange strategy.

[0094] Specifically, based on the system state matrix, a reinforcement learning model (RLM) is used to optimize the order and amount of water exchange between adjacent water tanks, resulting in a basic water exchange strategy (BWES). The reinforcement learning model uses the system state matrix as environmental observation, outputs control actions such as pump start-up and shutdown, and valve opening and closing, and uses minimizing energy consumption, balancing water levels, and meeting water demand as reward functions. Through interactive training with a virtual water network environment, the RLM learns how to determine which adjacent water tanks should transfer water and how much water should be transferred under complex conditions to achieve the optimization objective. The basic water exchange strategy reflects the current optimal decision made by the RLM.

[0095] Based on historical water usage data in a pre-set historical database, predict future water usage fluctuation trends and generate pre-regulation and storage control commands;

[0096] Specifically, the process involves predicting future water usage fluctuations based on historical water usage data and generating a Pre-storage Control Instruction (PCI). This includes storing and analyzing user water usage habits and patterns at different times and in different scenarios, such as peak water usage times after breakfast and dinner (washing, toilet use, etc.). Using time series analysis or machine learning prediction models, the system forecasts water demand for a future period, including the timing and approximate volume of water required. Based on the forecast results, a PCI is generated, which plans to pre-transfer and store an appropriate amount of water between water tanks before peak water usage arrives, ensuring a sufficient supply of usable water to quickly respond to demand during peak periods.

[0097] The basic water exchange strategy is integrated with the pre-regulation and storage control command to generate a water allocation command for dynamic balance optimization of the water network.

[0098] Specifically, this step integrates real-time optimization and forward-looking prediction. The basic water exchange strategy is fused with the pre-regulation control command to generate the water allocation command for dynamic balance optimization of the water network. This fusion process can be a priority judgment or a weighted combination. For example, if there is a conflict between the pre-regulation command and the basic water exchange strategy, the priority of the command or a compromise will be determined based on factors such as the current water tank level and the urgency of predicted demand. The final generated water allocation command is a comprehensive set of commands that includes not only water exchange suggestions optimized based on the current state but also pre-regulation arrangements considering future predicted demand, aiming to achieve overall dynamic balance and optimized operation of the RV water network.

[0099] Optionally, the allocation scheme based on the circulating water scheduling path further includes:

[0100] The system continuously monitors the graded purified water quality parameters. When any of the monitored graded purified water quality parameters exceeds the preset safety range, an isolation command signal is generated.

[0101] Specifically, the system continuously monitors the set of graded purified water parameters. When any monitored parameter exceeds a preset safety range, an isolation command signal is generated. The graded purified water output from the multi-stage membrane treatment unit is monitored in real time via online water quality sensors. Parameters include, but are not limited to, turbidity, total organic carbon, conductivity, specific ion concentration, and microbial count. These parameters each have their corresponding safety thresholds or ranges; for example, the turbidity of washing water should not exceed a certain value. When any monitored water quality parameter is detected to exceed its preset safety range, such as a sudden increase in TOC content, indicating an abnormal purification effect, an isolation command signal is immediately generated. This signal serves as a warning that the reclaimed water poses a safety risk and requires cessation of normal use.

[0102] Based on the isolation command signal, the abnormal water body is isolated and introduced for treatment. The isolation and introduction of the abnormal water body includes cutting off the original water supply path, activating the backup treatment channel, and introducing the abnormal water body into the secondary treatment chamber.

[0103] Specifically, based on the isolation command signal, the abnormal water body is isolated and treated. The isolation and treatment of the abnormal water body includes a series of emergency operations. First, the original water supply path is cut off, i.e., the water is stopped from being transported to the intended reuse route, achieved by closing relevant valves or stopping the pumps. Second, a backup treatment channel is activated; if a backup treatment unit or process is available, it is activated to prepare for the abnormal water body requiring secondary treatment. Finally, the abnormal water body is introduced into a secondary treatment chamber. By switching valves to control the water flow direction, the water, already marked as "abnormal," is directed to a dedicated secondary treatment chamber or temporary storage tank, rather than entering the reuse water tank, thus preventing contamination of existing qualified reclaimed water or direct use by users.

[0104] Based on the isolation command signal, abnormal feature parameters are recorded and the weight allocation of the contamination feature matrix is ​​corrected in reverse.

[0105] Specifically, after receiving the signal, the controller quickly closes the valve leading to the toilet flushing tank and opens the valve leading to the secondary treatment tank, directing the excessive wastewater to the secondary treatment process instead of having it directly used for flushing in the toilet flushing tank.

[0106] Based on the same inventive concept, this invention also provides an intelligent purification and recycling system for RV wastewater, such as... Figure 5 As shown, the system includes:

[0107] The dual-mode sensing and data fusion module is equipped with an optical sensor array and a bioelectrochemical sensor to acquire dual-mode detection signals of wastewater and perform synchronization processing to generate optical and electrochemical parameter data.

[0108] The pollution feature modeling module is used to receive the optical and electrochemical parameter data and generate a dynamically updated pollution feature matrix through analysis and modeling.

[0109] The intelligent purification control module is equipped with a multi-level membrane treatment unit and a controller, which is used to generate and execute a set of multi-level membrane treatment instructions based on the pollution feature matrix to obtain a set of graded purified water quality parameters.

[0110] The reuse path planning module is used to perform water quality assessment, path analysis and priority adjustment based on the graded purification water quality parameter set and related information, and generate a circulating water scheduling path allocation scheme.

[0111] The smart water network execution and protection module connects the sensors and actuators of the water network system. Based on the circulating water scheduling path allocation scheme, it performs dynamic balance optimization of the water network to generate water allocation instructions. Combined with real-time water quality safety monitoring results, it realizes the controlled and safe recycling of reclaimed water and the handling of abnormal responses.

[0112] It should be noted that the functional division and information interaction between the various modules described above are logical, but in terms of physical implementation, they can be integrated on the same software platform or deployed in a distributed manner. The connections between them represent data flow and control flow, aiming to collaboratively achieve the building energy consumption dynamic optimization goal of this invention. The above descriptions are merely exemplary embodiments of this invention and should not be construed as limiting the scope of protection of this invention.

Claims

1. A method for intelligent purification and recycling of RV wastewater, characterized in that, The method includes: Acquire wastewater dual-mode detection signals and synchronize and align the wastewater dual-mode detection signals to generate optical and electrochemical parameter data; Based on the optical and electrochemical parameter data, analysis and modeling are performed to generate a pollution feature matrix, which includes spectral attenuation rate, organic oxidation degree and particle size distribution characteristics. Based on the pollution feature matrix, a multi-level membrane treatment instruction set is executed to obtain a graded purification water quality parameter set. The multi-level membrane treatment instruction set sets the membrane module switching threshold and catalytic treatment parameters. Based on the analysis of available reuse pathways by matching the set of graded purified water quality parameters with the preset reuse standard set, and by combining the pre-collected dynamic environmental parameters, the priority order of the available reuse pathways is adjusted to generate a circulating water scheduling path allocation scheme. Based on the circulating water scheduling path allocation scheme, combined with the real-time acquired water network status information and water storage demand, a water allocation instruction for dynamic balance optimization of the water network is generated, and the circulating water scheduling path allocation scheme and the water allocation instruction are executed to control the graded transportation and recycling of reclaimed water.

2. The intelligent purification and recycling method for RV wastewater according to claim 1, characterized in that, The generated optical and electrochemical parameter data include: Reflectance spectrum data with continuously distributed wavelengths are acquired through an optical sensor array; Charge transfer impedance data of organic matter in wastewater were measured using a bioelectrochemical sensor. The reflection spectrum data and the charge transfer impedance data are aligned on the time axis to generate synchronously time-aligned optical and electrochemical parameter data.

3. The intelligent purification and recycling method for RV wastewater according to claim 2, characterized in that, The generated pollution feature matrix includes: The reflectance spectral data is decomposed into scattering and absorption components to determine the particle size distribution characteristics and spectral attenuation rate. The charge transfer impedance data is subjected to wavelet noise reduction and matched with a pollutant feature library to determine the organic oxidation degree index. A three-dimensional data space is constructed using the spectral attenuation rate, the organic oxidation degree index, and the particle size distribution characteristics. Based on the aforementioned three-dimensional data space, a convolutional neural network is used to calculate pollution weight factors and generate a dynamically updated pollution feature matrix.

4. The intelligent purification and recycling method for RV wastewater according to claim 3, characterized in that, The step of making decisions and executing a multi-level membrane treatment instruction set based on the pollution feature matrix includes: The catalytic treatment parameters are determined based on the organic matter oxidation degree index. Based on the particle size distribution characteristics and the chemical residue characteristics in the fouling characteristic matrix, the switching threshold and related operating parameters of the membrane module are determined. The catalytic treatment parameters, the membrane module switching threshold, and the relevant operating parameters are combined to generate a multi-level membrane treatment instruction set.

5. The intelligent purification and recycling method for RV wastewater according to claim 4, characterized in that, The step of making decisions and executing a multi-level membrane treatment instruction set based on the pollution feature matrix further includes: Based on the characteristics of chemical residues reflected in the pollution feature matrix, a reagent formula is matched and selected from a preset atomizing agent formula library; The reagent formulation is dispersed into micron-sized particles and injected into the water to be treated by a magnetically driven atomizing nozzle. The contact reaction time between the reagent formulation and the wastewater is dynamically adjusted by using the real-time change rate of the online oxidation-reduction potential monitoring value.

6. The intelligent purification and recycling method for RV wastewater according to claim 1, characterized in that, The generated circulating water scheduling path allocation scheme includes: The graded water quality parameter set is mapped to preset Class I, Class II, and Class III water quality levels; Based on the water quality grades and the preset path mapping relationship between each grade and the reuse system, the available reuse pathways are determined; Based on the available reuse pathways and the real-time water demand of the vehicle, the priority sequence is adjusted to generate a circulating water scheduling path allocation scheme.

7. The intelligent purification and recycling method for RV wastewater according to claim 6, characterized in that, The adjustment of the priority sequence based on the available reuse pathways and the real-time water demand of the vehicle includes: If the external ambient temperature is detected to exceed the preset high temperature threshold, the priority of the greening irrigation path will be increased. If the remaining capacity of the clean water tank is detected to be lower than the preset safety threshold, the priority of the drinking water replenishment route will be increased. When a vehicle is detected to be parked and has a need for bathroom water, it is assigned the highest priority to the bathroom system's water supply path.

8. The intelligent purification and recycling method for RV wastewater according to claim 1, characterized in that, The water allocation instructions for generating the dynamic balance optimization of the water network include: Real-time acquisition of a system status matrix including the capacity of each water tank, pipeline pressure, and pump energy consumption; Based on the system state matrix, a preset reinforcement learning model is used to optimize the water exchange sequence and value between adjacent water tanks to obtain a basic water exchange strategy. Based on historical water usage data in a pre-set historical database, predict future water usage fluctuation trends and generate pre-regulation and storage control commands; The basic water exchange strategy is integrated with the pre-regulation and storage control command to generate a water allocation command for dynamic balance optimization of the water network.

9. The intelligent purification and recycling method for RV wastewater according to claim 1, characterized in that, The method of allocating the circulating water scheduling path also includes: The system continuously monitors the graded purified water quality parameters. When any of the monitored graded purified water quality parameters exceeds the preset safety range, an isolation command signal is generated. Based on the isolation command signal, the abnormal water body is isolated and introduced for treatment. The isolation and introduction of the abnormal water body includes cutting off the original water supply path, activating the backup treatment channel, and introducing the abnormal water body into the secondary treatment chamber. Based on the isolation command signal, abnormal feature parameters are recorded and the weight allocation of the contamination feature matrix is ​​corrected in reverse.

10. A smart purification and recycling system for RV wastewater, applied to the smart purification and recycling method for RV wastewater as described in any one of claims 1 to 9, characterized in that, The system includes: The dual-mode sensing and data fusion module is equipped with an optical sensor array and a bioelectrochemical sensor to acquire dual-mode detection signals of wastewater and perform synchronization processing to generate optical and electrochemical parameter data. The pollution feature modeling module is used to receive the optical and electrochemical parameter data and generate a dynamically updated pollution feature matrix through analysis and modeling. The intelligent purification control module is equipped with a multi-level membrane treatment unit and a controller, which is used to generate and execute a set of multi-level membrane treatment instructions based on the pollution feature matrix to obtain a set of graded purified water quality parameters. The reuse path planning module is used to perform water quality assessment, path analysis and priority adjustment based on the graded purification water quality parameter set and related information, and generate a circulating water scheduling path allocation scheme. The smart water network execution and protection module connects the sensors and actuators of the water network system. Based on the circulating water scheduling path allocation scheme, it performs dynamic balance optimization of the water network to generate water allocation instructions. Combined with real-time water quality safety monitoring results, it realizes the controlled and safe recycling of reclaimed water and the handling of abnormal responses.

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