Intelligent purification and circulation method and system for waste water of motor home

Abstract

The invention discloses an intelligent purification and circulation method and system for waste water of a motor home, and belongs to the technical field of waste water treatment and reuse. Generating a matrix containing key pollution characteristics based on the parameters; executing multi-stage membrane treatment according to the matrix decision to obtain graded water quality; based on a water quality matching reuse standard, a scheduling scheme is generated by combining requirements and environment adjustment priorities; the scheme and the water network state are combined to optimize water volume allocation, and efficient recycling of recycled water is controlled. According to the invention, dual-mode sensing fusion analysis, dynamic pollution characteristic identification, multi-stage membrane intelligent cooperative processing and a water network balance optimization scheduling strategy are adopted, efficient deep purification, safe grading recycling and intelligent management of motor home wastewater can be realized, and the utilization rate of water resources, the intelligent level of system operation and the user experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment and reuse, and in particular to an intelligent purification and recycling method and system for RV wastewater. Background Art

[0002] Recreational Vehicles (RVs) provide users with integrated mobile living and travel solutions. Inevitably, domestic wastewater is generated during daily use, mainly including gray water produced by washing and cooking, and black water discharged from toilets. Given the mobile nature of RVs and increasingly strict environmental emission regulations, effective on-board management of this wastewater has become a basic requirement for the normal use of RVs. A typical RV water system usually includes a fresh water tank, gray water and black water collection tanks, and a pipeline system connecting various water use points.

[0003] However, the current wastewater treatment strategies commonly used in the RV field often show deficiencies in practical applications. The purification effects provided by many existing on-board treatment devices are limited, and the water quality stability after treatment is insufficient, often making it difficult to meet the diverse reusing water quality standards. Therefore, users need to frequently search for designated sewage discharge points to dump the collected wastewater, which to a certain extent limits the continuous travel ability of RVs. In addition, existing systems usually lack a mechanism for hierarchical utilization based on the treated water quality, and there is also a lack of a mature solution for effectively allocating and balancing the water volumes between various tanks. Summary of the Invention

[0004] To solve the above problems, the present invention provides an intelligent purification and recycling method and system for RV wastewater. By adopting dual-mode sensing fusion analysis, dynamic pollution feature recognition, multi-stage membrane intelligent collaborative treatment, and water network balance optimization scheduling strategies, it can achieve efficient and in-depth purification and safe hierarchical reuse of RV wastewater, significantly improving the water resource utilization rate and the intelligent level of system operation.

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

[0006] An intelligent purification and recycling method for the wastewater of a motorhome, comprising obtaining dual-mode detection signals of the wastewater, synchronously aligning the timing of the dual-mode detection signals of the wastewater to generate optical and electrochemical parameter data; analyzing and modeling based on the optical and electrochemical parameter data to generate a pollution feature matrix, where the pollution feature matrix includes a spectral attenuation rate, an organic matter oxidation degree, and a particle size distribution feature; making a decision based on the pollution feature matrix and executing a multi-stage membrane treatment instruction set to obtain a hierarchical purification water quality parameter set, where the multi-stage membrane treatment instruction set sets a membrane module switching threshold and catalytic treatment parameters; matching a preset reuse standard set based on the hierarchical purification water quality parameter set to analyze available reuse paths, adjusting the priority order of the available reuse paths in combination with pre-collected dynamic environmental parameters to generate a circulating water scheduling path allocation plan; according to the circulating water scheduling path allocation plan, combining the water network status information and water storage requirements obtained in real time to generate a water volume allocation instruction for dynamic balance optimization processing of the water network, and executing the circulating water scheduling path allocation plan and the water volume allocation instruction to control the hierarchical transportation and recycling of reclaimed water.

[0007] Optionally, the generating of the optical and electrochemical parameter data includes: collecting reflected spectral data with continuously distributed wavelengths through an optical sensor array; measuring charge transfer impedance data of organic matters in the wastewater through a bioelectrochemical sensor; aligning the reflected spectral data and the charge transfer impedance data on the time axis to generate synchronously time-aligned optical and electrochemical parameter data.

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

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

[0010] Optionally, the decision-making and execution of the multi-level membrane treatment instruction set according to the pollution characteristic matrix further includes: matching and selecting a chemical agent formula from a preset atomized chemical agent formula library according to the chemical residue characteristics reflected in the pollution characteristic matrix; dispersing the chemical agent formula into micron-sized particles through a magnetically driven atomizing nozzle and injecting the particles into the water to be treated, and dynamically correcting the contact reaction time between the chemical agent formula and the wastewater by using the real-time change rate of the online oxidation-reduction potential monitoring value.

[0011] Optionally, the generation of the circulating water scheduling path allocation plan includes: mapping the hierarchical purification water quality parameter set to preset water quality grades of Class I, Class II, and Class III; determining the available reuse paths based on the water quality grades and the preset path mapping relationship between each grade and the reuse system; adjusting the priority sequence according to the available reuse paths and the on-vehicle real-time water demand to generate a circulating water scheduling path allocation plan.

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

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

[0014] Optionally, the process according to the circulating water scheduling path allocation plan further includes: continuously monitoring the hierarchical purification water quality parameters, and generating an isolation instruction signal when any parameter in the monitored hierarchical purification water quality parameter set exceeds a preset safety range; according to the isolation instruction signal, performing isolation and import processing on the abnormal water body, and the isolation and import processing of the abnormal water body includes cutting off the original water transmission path, enabling a standby treatment channel, and introducing the abnormal water body into a secondary treatment chamber; based on the isolation instruction signal, recording the abnormal characteristic parameters and reversely correcting the weight allocation of the pollution characteristic matrix.

[0015] Based on the same inventive concept, the present 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 characteristic modeling module, for receiving the optical and electrochemical parameter data and generating a dynamically updated pollution characteristic matrix through analysis and modeling; an intelligent purification control module, configured with a multi-stage membrane treatment unit and a controller, for generating and executing a set of multi-stage membrane treatment instructions based on the decision of the pollution characteristic matrix to obtain a set of hierarchical purification water quality parameters; a reuse path planning module, for performing water quality assessment, path analysis and priority adjustment based on the set of hierarchical purification water quality parameters and related information to generate a recycling water scheduling path allocation plan; a smart water network execution and guarantee module, connecting the sensors and actuators of the water network system, performing dynamic balance optimization of the water network according to the recycling water scheduling path allocation plan to generate a water volume allocation instruction, and realizing the controlled, safe recycling and abnormal response processing of reclaimed water in combination with the real-time water quality safety monitoring results.

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

[0017] 1. It improves the 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 characteristic matrix, the present invention can comprehensively and real-time grasp the wastewater quality characteristics, overcome the defect of one-sided information of traditional single detection means, and provide a reliable basis for subsequent precise treatment;

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

[0019] 3. It optimizes the efficiency and management level of reclaimed water recycling. The present invention not only scientifically grades the purified water, but also establishes an intelligent mapping between water quality and reuse paths, and dynamically adjusts the water conveyance priority and optimizes the water volume allocation in the water tank in combination with factors such as the environment and demand, realizing the maximization and refinement of reclaimed water resources, which is significantly better than the traditional extensive reuse or discharge mode;

[0020] 4. It improves the reliability, safety and intelligence of system operation. The integrated water quality anomaly rapid detection and isolation and reprocessing mechanism effectively guarantees water use safety, while the water network dynamic balance algorithm based on reinforcement learning and prediction improves the system's self-optimization and stable operation ability, making the entire water system more intelligent, reliable and efficient.

[0021] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention can be realized and attained by the structure particularly pointed out in the specification, claims as well as the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 is a schematic flow chart of a method for intelligent purification and recycling of waste water in a recreational vehicle according to an embodiment of the present invention.

[0024] Figure 2 is a comparison chart of hierarchical purification water quality parameter sets according to an embodiment of the present invention.

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

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

[0027] Figure 5 is a schematic structural diagram of a system for intelligent purification and recycling of waste water in a recreational vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Referring to Figure 1 , an embodiment of the present invention provides a method for intelligent purification and recycling of waste water in a recreational vehicle. By adopting an optical and electrochemical dual-mode sensing fusion analysis, dynamic pollution characteristic recognition, multi-stage membrane treatment intelligent control, and water network balance optimization scheduling strategy, it can achieve efficient and in-depth purification, safe hierarchical reuse, and intelligent management of waste water in a recreational vehicle, significantly improving the water resource utilization rate and user experience of the recreational vehicle.

[0030] The specific steps of the method in this embodiment are as follows:

[0031] Obtain the dual-mode detection signal of the wastewater, and perform synchronous timing alignment on the dual-mode detection signal of the wastewater to generate optical and electrochemical parameter data;

[0032] Specifically, the dual-mode detection signal of the wastewater refers to the original measurement signal of the wastewater obtained through at least two sensing technologies based on different physical or chemical principles, such as optical means and electrochemical means. Synchronous timing alignment is the process of unifying these signals from different sources and possibly different sampling frequencies on a time basis. Common methods include timestamp alignment or data resampling based on interpolation algorithms to ensure the accuracy of subsequent data fusion analysis. The generated optical and electrochemical parameter data after processing is a structured data set, containing multi-dimensional information reflecting the current state of the wastewater, serving as the basis for subsequent analysis and modeling.

[0033] Analyze and model based on the optical and electrochemical parameter data to generate a pollution characteristic matrix, where the pollution characteristic matrix includes a spectral attenuation rate, an organic matter oxidation degree, and a particle size distribution characteristic;

[0034] Specifically, this step aims to extract key information from the parameter data obtained in the previous step and construct a mathematical model that can quantitatively characterize the main pollution situation of the wastewater, namely the pollution characteristic matrix. This matrix is a dynamically updated data structure, and its core elements include: the spectral attenuation rate, which reflects the attenuation of light when propagating in the wastewater and is related to chromaticity and dissolved organic matter concentration; the organic matter oxidation degree, which is an index to measure the total amount of organic pollutants in the wastewater or the ease of its oxidation treatment; and the particle size distribution characteristic, which describes the size and distribution of suspended particles in the wastewater. The quantified values or relative weights of these characteristics constitute the pollution characteristic matrix, providing a decision-making basis for subsequent purification treatment.

[0035] Make a decision based on the pollution characteristic matrix and execute a set of multi-stage membrane treatment instructions to obtain a set of hierarchical purification water quality parameters, where the set of multi-stage membrane treatment instructions sets the membrane module switching threshold and catalytic treatment parameters;

[0036] Specifically, based on the real-time updated pollution characteristic matrix, a set of adaptive multi-stage membrane treatment instruction sets are 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 generally refers to the combined use of different levels of membrane separation technologies such as ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), etc., and may be supplemented by advanced oxidation processes such as photocatalytic oxidation (PCM). The content of the instruction sets mainly includes setting the operating parameters of each treatment unit, specifically reflected as catalytic treatment parameters, such as the excitation intensity or reaction residence time of the ultraviolet (UV) lamp in the PCM reactor; and the membrane module switching threshold and related operating parameters, and setting its operating pressure or backwashing frequency, etc. The controller executes the instruction sets, drives the corresponding physical treatment units to operate, and real-time monitors the purification effect through an on-line water quality sensor, and finally obtains a hierarchical purified water quality parameter set, and these parameters may include key indicators such as turbidity, total organic carbon (TOC), and conductivity. The comparison of the hierarchical purified water quality parameter sets of different water quality levels is as Figure 2 shown.

[0037] Based on the hierarchical purified water quality parameter set, match the preset reuse standard set to analyze the available reuse paths, and combine the pre-collected dynamic environmental parameters to adjust the priority order of the available reuse paths, and generate a circulating water scheduling path allocation plan;

[0038] Specifically, the obtained hierarchical purified water quality parameter set is matched with the stored preset reuse standard set, and this standard set defines the specific water quality requirements for different water use scenarios in the RV, such as washing, flushing the toilet, greening irrigation, etc. Through matching analysis, it is determined which standards the current purified water meets, so as to analyze and obtain a set of available reuse paths. Then, dynamic environmental parameters will be collected, and these parameters may include the external environmental temperature, the current remaining capacity of the fresh water tank, the operating state of the vehicle, i.e., the parked or driving state, etc., and combined with the user's real-time water use demand signal, such as the trigger signal from the faucet or toilet, through the preset priority rules or algorithms, the real-time priority order of all current available reuse paths is adjusted. Finally, a dynamic circulating water scheduling path allocation plan with clear priorities is generated, and this plan provides decision-making guidance for subsequent water volume allocation and transportation.

[0039] According to the circulating water scheduling path allocation scheme, combined with the real-time obtained water network status information and water storage requirements, generate a water volume allocation instruction for the dynamic balance optimization of the water network, and execute the circulating water scheduling path allocation scheme and the water volume allocation instruction to control the hierarchical transportation and recycling of reclaimed water.

[0040] Specifically, this is the final control and execution link. The control core is based on the circulating water scheduling path allocation scheme generated in the previous step, which clarifies the flow direction target and priority of the purified water. At the same time, the water network status information is obtained in real time through the sensor network. These information include the current liquid level or capacity of each water tank in the RV, such as the gray water tank, black water tank, various purification water tanks, fresh water tank, etc., as well as the key node pressure of the connecting pipeline and the real-time energy consumption of each water pump. Combining these status information and the water storage requirements of the user, such as giving priority to ensuring the water volume in the fresh water tank or meeting the needs of specific water use points, run the dynamic balance optimization algorithm of the water network. The purpose of this algorithm is to generate the optimal water volume allocation instruction, which not only includes the water transportation instruction required to meet the highest priority path in the scheduling scheme, but also can include the optimization instruction for transferring the water volume between each water tank to achieve the overall water level balance, reduce energy consumption or prepare for subsequent water use. Finally, the controller executes the circulating water scheduling path allocation scheme and the generated water volume allocation instruction, and realizes the hierarchical transportation and recycling of reclaimed water according to the planned level, path and optimized water volume by precisely controlling the start-stop and rotation speed of the relevant water pumps, and the opening and closing state of the pipeline solenoid valves. The change of the water tank capacity in the dynamic balance optimization process of the RV water network is as Figure 3 shown.

[0041] The present invention realizes the full-process closed-loop intelligent management of the RV wastewater from generation to reclamation and utilization through integrated sensing, intelligent analysis and decision-making, adaptive processing control and global optimized scheduling management, which not only improves the recycling rate of water resources, reduces the impact of wastewater discharge on the environment, but also greatly improves the water use convenience of RV users and the endurance ability of long-distance travel.

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

[0043] Collecting reflection spectrum data with continuously distributed wavelengths through an optical sensor array;

[0044] Specifically, the reflected spectral data with a continuous wavelength distribution is collected by an Optical Sensor Array (OSA). The optical sensor array usually consists of one or more multi-wavelength light sources or tunable light sources and supporting spectral detectors. The light source emits light covering a specific wavelength range towards the wastewater area to be processed. When the light irradiates the wastewater surface or passes through the wastewater, part of the light will be affected by various substances in the wastewater. The reflected spectral data is the record of the change in the intensity of the light signal reflected from the wastewater surface or inside over time at different wavelengths. These spectral data contain information reflecting the color, turbidity of the wastewater, and the absorption or scattering characteristics of certain chemical components at specific wavelengths. For example, the turbidity T of the wastewater may have an approximate linear or non-linear relationship with the intensity of the scattered light turb at a specific wavelength λ , while the concentration C of certain organic substances org may be related to the intensity of the absorbed light uv at a specific ultraviolet wavelength λ . The collected original reflected spectral data is usually represented as a high-dimensional time series data, where each time point corresponds to a vector containing the light intensity values at multiple wavelengths.

[0045] Determine the charge transfer impedance data of organic substances in the wastewater through a bioelectrochemical sensor;

[0046] Specifically, the bioelectrochemical sensor usually includes a working electrode, a counter electrode, and a reference electrode, and a bioactive material sensitive to specific organic substances, such as a microbial community or a purified enzyme, is immobilized on the surface of the working electrode. When the wastewater flows through the sensor, the organic substances in the wastewater undergo a biocatalytic reaction under the action of the bioactive material, accompanied by the transfer of electrons at the electrode interface. The Electrochemical Impedance Spectroscopy (EIS) technique is used to detect the kinetic characteristics 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 the complex plane or logarithmic coordinate system and fitting an equivalent circuit model, the value of the charge transfer impedance (Rct) can be extracted. The level of the charge transfer impedance value is related to the reaction rate of the organic substances on the electrode surface, and thus can indirectly reflect the concentration or biochemical activity of the electrochemically reactive organic substances in the wastewater. The equivalent circuit model can be a complex distributed circuit model or a simplified model. For a simplified Randles circuit, the relationship between its total impedance Z(ω) and the angular frequency ω is as follows:

[0047]

[0048] where Z(ω) is the total impedance at the angular frequency ω, R s is the solution resistance, R ct 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, C dl is the double-layer capacitance, α is the dispersion index of the Constant Phase Element (CPE) used to describe non-ideal capacitance behavior, and W(ω) is the impedance describing the diffusion process on the electrode surface.

[0049] Align the reflection spectral data and the charge transfer impedance data along the time axis to generate optically and electrochemically parameter data with synchronous time series alignment.

[0050] Specifically, the alignment of the reflection spectral data and the charge transfer impedance data along the time axis is a key preprocessing step before data fusion, aiming to map data from different sources to a unified time reference. This is usually achieved by attaching an accurate timestamp to each acquired data point. Then, a common time resolution or sampling rate is selected as the alignment target, such as once per second.

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

[0052] Decompose the reflection spectral data into a scattering component and an absorption component, and determine the particle size distribution characteristics and the spectral attenuation rate;

[0053] Specifically, the reflection spectral data is decomposed into a scattering component (Scattering Component, SC) and an absorption component (Absorption Component, AC), and on this basis, the particle size distribution characteristics (Particle Size Distribution, PSD) of the suspended particles in the wastewater and the spectral attenuation rate (Spectral Attenuation Rate, SAR) of the liquid are determined. When a broadband light beam irradiates the wastewater, the loss of light energy is mainly due to the scattering by the suspended particles in the wastewater and the absorption by the dissolved substances. The reflection spectral data contains the composite information of these interactions. To separate and quantify these effects, spectral signal processing techniques can be applied. For example, by establishing a radiative transfer model that describes the transmission of light in a scattering and absorbing medium, or by using a multivariate decomposition algorithm such as non-negative matrix factorization (Non-negative Matrix Factorization, NMF), the measured reflection spectral data can be resolved into two independent spectral curves that mainly represent the scattering contribution and the absorption contribution. The shape and intensity of the scattering component spectral curve are closely related to the concentration, size, shape, and optical properties of the suspended particles in the wastewater. Based on the scattering component, combined with optical models such as Mie scattering theory, the particle size distribution characteristics of the suspended particles in the wastewater can be inversely calculated. For example, for particles assumed to be spherical, the scattering cross-section σ scat (λ, d) is the core output of Mie scattering theory, and the total scattering intensity I scatter (λ) is the superposition of the scattering effects of all different-sized particles in the system. In a particle ensemble, 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] where, I scatter (λ) represents the total scattering intensity at wavelength λ, represents the integral of the particle diameter d from the minimum diameter d min to the maximum diameter d max range, N(d) represents the number concentration of particles with diameter d in the wastewater, and σ scat(λ, d) represents the scattering cross - section of particles with diameter d at wavelength λ, and ∫dd represents the integration with respect to d. By solving or fitting the above - mentioned equation, the particle size distribution function N(d) or its statistical parameters, such as the average particle size, particle size range, etc., can be estimated from I scatter (λ). These are the so - called particle size distribution characteristics. The absorption component spectral curve is mainly related to the absorption characteristics of dissolved substances in the wastewater and usually shows absorption peaks in a specific wavelength range. Based on the absorption component, the spectral attenuation rate SAR of the liquid can be calculated, which comprehensively reflects the attenuation degree caused by the absorption and scattering of light in the wastewater.

[0056] Perform wavelet noise reduction processing on the charge transfer impedance data and match it with the pollutant feature library to determine the organic matter oxidation degree index;

[0057] Specifically, perform wavelet noise reduction processing on the charge transfer impedance data. During the acquisition process, the bio - electrochemical sensor is easily interfered by various factors, such as power fluctuations, environmental noise, instantaneous changes in biofilm activity, etc. These interferences introduce noise and affect the accuracy of the charge transfer impedance data. Wavelet noise reduction is an effective signal denoising method. It decomposes the original signal at different frequency scales, identifies the high - frequency components where the noise is located, performs threshold processing on these components, and finally reconstructs through inverse transformation to obtain a smooth signal that retains the main features. The denoised charge transfer impedance data can more accurately reflect the biochemical reactions or electrochemical activities of organic matter in the 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 matter oxidation degree index (Organic Oxidation Degree Index, OODI). The pollutant feature library is a database that stores the standard electrochemical impedance spectra or their extracted characteristic values corresponding to various typical RV wastewater pollutants. The matching process involves comparing the current denoised charge transfer impedance data or the extracted features from it with the standard features stored in the library and calculating the similarity or distance between them. For example, the Euclidean distance or correlation coefficient between the feature vectors of the current data and each entry in the library can be calculated to find the closest match. According to the matched library entry and its corresponding known information, a quantified organic matter oxidation degree index is determined. This index aims to reflect the content, structural complexity of total organic matter in the wastewater or the ease of its oxidation treatment. For example, matching the characteristics of easily degradable organic matter with a lower impedance results in a higher score for the organic matter oxidation degree index.

[0058] Construct a three - dimensional data space through the spectral attenuation rate, the organic matter oxidation degree index, and the particle size distribution characteristics;

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

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

[0061] Specifically, based on the data stream of 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 three-dimensional feature vectors continuously sampled within a time window is used as the input and 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 pollution levels. The pollution weight factor is a vector, and each of its elements represents the confidence or probability that the wastewater belongs to a preset pollution category or 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 levels. By adjusting the model parameters, it can accurately map the input three-dimensional feature space patterns to the correct pollution weight factor output.

[0062] Optionally, the decision-making and execution of the multi-level membrane treatment instruction set according to the pollution feature matrix includes:

[0063] Determine the catalytic treatment parameters according to the organic matter oxidation degree index;

[0064] Specifically, the catalytic treatment parameters are determined according to the organic matter oxidation degree index. The organic matter oxidation degree index reflects the relative content of total organic matter in the wastewater and its degree of being easily oxidized. Catalytic treatment usually refers to advanced oxidation processes, and these processes need to adjust the operating parameters according to the characteristics of the organic matter to be treated to achieve the best treatment effect and energy consumption efficiency.

[0065] Determine the membrane module switching threshold and related operating parameters according to the particle size distribution characteristics and the chemical residue characteristics in the pollution characteristic matrix;

[0066] Specifically, the chemical residue characteristics in the pollution characteristic matrix, such as the presence of surfactants, heavy metal ions or specific organic chemicals and their weighting factors, affect the selection fineness of the membrane, the potential membrane fouling risk, and the required transmembrane pressure or backwashing strategy. In this step, by evaluating the current particle size distribution characteristics, such as the average particle size or particle size range, and combining the types and concentrations of chemical residues reflected in the pollution characteristic matrix, through a preset decision logic or look-up table, determine the type of membrane module to be used currently and its switching threshold. At the same time, set the related operating parameters of this membrane module, the frequency and intensity of backwashing or chemical cleaning, and the influent flow rate, etc., to maximize the membrane flux, extend the service life and ensure the purification effect.

[0067] Combine the catalytic treatment parameters, the membrane module switching threshold, and the related operating parameters to generate a multi-level membrane treatment instruction set.

[0068] Specifically, the catalytic treatment parameters, the membrane module switching threshold, and the related operating parameters are combined to generate the multi-level membrane treatment instruction set. This instruction set is a structured data packet that contains the specific operating instructions for each membrane treatment unit and catalytic treatment unit in the control during the current or future period of time. These instructions include, but are not limited to, starting or stopping a certain membrane module, setting the target value of the operating pressure of a specific membrane module, setting the ultraviolet lamp power or operating time of the catalytic reactor, setting the trigger conditions or cycles of backwashing or chemical cleaning, adjusting the flow rate of the feed pump, etc. The instruction set is dynamically generated according to the real-time pollution characteristic matrix, aiming to make the multi-level membrane treatment unit and the catalytic treatment unit work together to remove the pollutants in the current wastewater in the most optimized and effective way to achieve the expected hierarchical purified water quality target, as shown in the pollution characteristic matrix heat map, such as Figure 4 shown.

[0069] Optionally, the decision-making and execution of the multi-level membrane treatment instruction set according to the pollution characteristic matrix further includes:

[0070] Match and select a chemical agent formula from a preset atomized chemical agent formula library according to the chemical residue characteristics reflected in the pollution characteristic matrix;

[0071] Specifically, this is an auxiliary chemical treatment step carried out when necessary. According to the characteristics of chemical residues reflected in the pollution characteristic matrix, a chemical agent formula is matched and selected from a preset atomized chemical agent formula library. The pollution characteristic matrix is evaluated by a machine learning model and can quantify the weight factors of specific refractory chemical substances or components with potential membrane pollution risks present in the wastewater. When these weight factors exceed a preset threshold, it is determined that additional chemical treatment is required to degrade or modify these substances. The atomized chemical agent formula library stores a variety of optimized chemical agent formulas for common chemical residues in RVs, such as trace formulas based on hydrogen peroxide, ozone, or specific catalysts. According to the specific types and concentrations of chemical residues indicated in the pollution characteristic matrix, the most suitable chemical agent formula for treating the current pollutants is searched for and selected in this library.

[0072] Through a magnetic drive atomizing nozzle, the chemical agent formula is dispersed into micron-sized particles and injected into the water to be treated, and the contact reaction time between the chemical agent formula and the wastewater is dynamically corrected using the real-time change rate of the online oxidation-reduction potential monitoring value.

[0073] Specifically, this step describes the dosing method of the selected chemical agent and reaction control. Through a magnetic drive atomizing nozzle, the chemical agent formula is dispersed into micron-sized particles and injected into the water to be treated. The magnetic drive atomizing nozzle uses high-frequency vibration or centrifugal force to break the liquid chemical agent into fine droplets with a size in the micron range, significantly increasing the contact surface area between the chemical agent and the wastewater and improving the reaction efficiency and chemical agent utilization rate. The chemical agent is directly sprayed or injected into the wastewater flow to be treated in an atomized form. At the same time, the contact reaction time between the chemical agent formula and the wastewater is dynamically corrected using the real-time change rate of the online oxidation-reduction potential (ORP) monitoring value. The oxidation-reduction potential is an indicator to measure the strength of the oxidizing or reducing property of a solution, and a chemical oxidation reaction will cause a change in the ORP value. By real-time monitoring the change rate of the ORP value, the reaction process between the chemical agent and the organic matter can be judged. When the change rate of the ORP value decreases or tends to be stable, it indicates that the reaction is approaching completion, and based on this, the residence time of the wastewater in the reaction chamber or the start time of subsequent treatment can be dynamically adjusted to ensure that the chemical agent reacts fully while avoiding excessive dosing.

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

[0075] Mapping the hierarchical purified water quality parameter set to the preset Class I, Class II, and Class III water quality grades;

[0076] Specifically, this step is to conduct a standardized assessment of the purified water quality. The graded purified water quality parameter set is mapped to the preset water quality grades of Class I, Class II, and Class III. In the RV water circulation system, different requirements for water quality may be imposed according to different reuse paths, such as drinking, washing, flushing toilets, or greening irrigation. The preset water quality grades of Class I, Class II, and Class III correspond to the purification degrees required for these different reuse paths, with Class I water quality being the highest and Class III water quality being the lowest. The graded purified water quality parameter set 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 grades to determine which water quality grade the current purified water belongs to.

[0077] Based on the water quality grade and the preset path mapping relationship between each grade and the reuse system, determine the available reuse paths;

[0078] Specifically, this step determines the feasible reuse directions according to the water quality assessment results. Based on the water quality grade and the preset path mapping relationship between each grade and the reuse system, determine the available reuse paths. A mapping table or rule set is stored in the memory, which specifies which specific water use scenarios or reuse systems in the RV each water quality grade can be safely used for. For example, Class I water quality can be mapped to all reuse paths, Class II water quality is mapped to washing and flushing toilets, and Class III water quality is only mapped to flushing toilets and greening. According to the current purified water quality grade determined in the previous step, query this mapping relationship to determine the set of all available reuse paths to which this batch of purified water can safely flow at the current moment.

[0079] Adjust the priority sequence according to the available reuse paths and the on-vehicle real-time water demand, and generate a circulating water scheduling path allocation plan.

[0080] Specifically, this step is to make the final reclaimed water allocation decision. Adjust the priority sequence according to the available reuse paths and the on-vehicle real-time water demand, and generate a circulating water scheduling path allocation plan. Although it is determined which paths are available, the demands of not all available paths are equally important or urgent. All available reuse paths, the user's real-time water demand, and the on-vehicle environmental parameters will be comprehensively considered. Through the preset priority rules or algorithms, such as giving priority to meeting the drinking water demand, followed by washing, then flushing toilets, and finally greening, the real-time priority ranking of all available paths is carried out. The finally generated circulating water scheduling path allocation plan is an ordered list of reuse paths, which clarifies which water quality grade of water should be preferentially delivered to which water use point and the corresponding water volume allocation target, so as to guide the actual operation of the water network.

[0081] Exemplarily, the purification system produces water of Class II quality. According to the mapping relationship, Class II water can be used for washing and flushing toilets. At this time, the user presses the toilet flushing button, and a flushing demand is detected. According to the priority rules, a scheduling plan is generated to allocate this batch of Class II water to the flushing water tank, with the highest priority.

[0082] Optionally, the adjusting the priority sequence according to the available reuse paths and the real-time water consumption demand of the vehicle includes:

[0083] When it is detected that the external environmental temperature exceeds a preset high temperature threshold, the priority of the greening irrigation path is increased;

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

[0085] When it is detected that the remaining capacity of the fresh water tank is lower than a preset safety threshold, the priority of the drinking water replenishment path is increased;

[0086] Specifically, when it is detected that the remaining capacity of the fresh water tank is lower than a preset safety threshold, the priority of the drinking water replenishment path is increased. The liquid level sensor of the fresh water tank continuously monitors the water storage capacity of the RV fresh water tank. The fresh water tank is the main source of drinking water and domestic water for the RV. When its remaining capacity drops below the preset safety threshold, such as below 20% of the total capacity, it indicates that the fresh water resources are in short supply. At this time, if reclaimed water of Class I quality can be produced, the priority of the drinking water replenishment path will be immediately increased to the highest level to ensure that once qualified reclaimed water is produced, it will be preferentially replenished into the fresh water tank to guarantee the drinking water safety of the user.

[0087] When it is detected that the vehicle is in a parked state and there is a demand for bathroom water, the first priority is assigned to the water supply path of the bathroom system.

[0088] Specifically, when it is detected that the vehicle is in a parked state and there is a demand for bathroom water, the highest priority is assigned to the water supply path of the bathroom system. The position and motion sensors of the RV can determine whether the vehicle is in a driving state or a parked state. Bathroom water consumption usually occurs when the vehicle is parked. When it is detected that the vehicle is in a parked state, and a water usage trigger signal from the bathroom system is received, and at the same time it is determined that there is reclaimed water meeting the bathroom water use standard available, the priority of the available path of water supply to the bathroom system will be immediately assigned as the highest to ensure that the user can obtain the reclaimed water meeting the demand in time.

[0089] Exemplarily, the RV is parked in the wild. The external temperature rises to 32 degrees Celsius, and at the same time, the capacity of the fresh water tank drops to 15%. The purification system has just produced Class II water that can be used for washing. According to the priority rules, the priority of greening irrigation is moderately increased due to high temperature, and the priority of drinking water replenishment triggered by the low level of the fresh water tank is the highest. The user turns on the faucet of the washbasin at this time. It is determined that the vehicle is parked and there is a bathroom use demand, and there is Class II water available. So the priority of the bathroom path is set to the highest, and the Class II water is immediately delivered to the washbasin.

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

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

[0092] Specifically, the real-time collection of the system state matrix (System State Matrix, SSM) including the capacity of each water tank, pipeline pressure, and pump energy consumption. The system state matrix is a data structure that is updated in real time and is obtained by sensors arranged throughout the RV water network. These sensors include capacity sensors that measure the current liquid level of each storage water tank, pressure sensors that monitor the water pressure of key pipe sections, and energy consumption sensors that monitor the real-time power consumption of each water pump. The system state matrix integrates the data of these heterogeneous sensors and comprehensively and real-time reflects the physical operation status and resource distribution of the current RV water circulation system.

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

[0094] Specifically, based on the system state matrix, use a reinforcement learning model (Reinforcement Learning Model, RLM) to optimize the water volume exchange sequence and value between adjacent water tanks to obtain a basic water volume exchange strategy (Basic Water Exchange Strategy, BWES). The reinforcement learning model uses the system state matrix as the environmental observation, uses control actions such as pump start / stop and valve opening / closing as the output, and uses minimizing energy consumption, balancing water levels, and meeting water use demands as the reward function. By interacting and training with the virtual water network environment, the RLM learns how to decide which adjacent water tanks should transfer water bodies and how much water to transfer under complex states to achieve the optimization goal. The basic water volume exchange strategy is the embodiment of the current optimal decision of the RLM.

[0095] Predict the future water use fluctuation trend according to the historical water use data in the preset historical database and generate a pre-adjustment and storage control instruction;

[0096] Specifically, the water use fluctuation trend in the future period is predicted based on historical water use data to generate a pre-storage control instruction (PCI). The water use habits and patterns of users at different times and in different scenarios are stored and analyzed, such as the peak water use for washing and toileting after breakfast and dinner. Time series analysis or machine learning prediction models are used to predict the water use demand in a future period, including the time points and approximate water volumes when water use demands occur. Based on the prediction results, a pre-storage control instruction is generated, which plans to transfer and store an appropriate amount of water between water tanks in advance before the arrival of the water use peak to ensure that there is sufficient available water source to quickly respond to the demand during the peak period.

[0097] The basic water volume exchange strategy is integrated with the pre-storage control instruction to generate a water volume allocation instruction for optimizing the dynamic balance of the water network.

[0098] Specifically, this step integrates immediate optimization and forward-looking prediction. The basic water volume exchange strategy is integrated with the pre-storage control instruction to generate the water volume allocation instruction for optimizing the dynamic balance of the water network. The integration process can be a priority judgment or a weighted combination. For example, if there is a conflict between the pre-storage instruction and the basic water volume exchange strategy, factors such as the current water tank liquid level and the urgency of the predicted demand are used to determine which instruction takes precedence or how to make a compromise. The finally generated water volume allocation instruction is a comprehensive instruction set that not only includes water volume exchange suggestions optimized based on the current state but also pre-storage arrangements considering future predicted demands, aiming to achieve the overall dynamic balance and optimized operation of the RV water network.

[0099] Optionally, the allocation scheme according to the recycled water scheduling path further includes:

[0100] Continuously monitor the hierarchical purification water quality parameters, and generate an isolation instruction signal when any of the monitored hierarchical purification water quality parameters exceeds the preset safety range;

[0101] Specifically, the hierarchical purification water quality parameter set is continuously monitored, and an isolation instruction signal is generated when any of the monitored parameters exceeds the preset safety range. The hierarchical purification water quality output from the multi-stage membrane treatment unit is real-time monitored through an on-line water quality sensor, including but not limited to parameters such as turbidity, total organic carbon, conductivity, specific ion concentration, and microbial count. Each of these parameters has its corresponding safety threshold or range. For example, the turbidity of washing water should not be higher than a certain value. When it is detected that any of the monitored water quality parameters exceeds its preset safety range, such as a sudden increase in the TOC content, indicating that the purification effect is abnormal, an isolation instruction signal is immediately generated. This signal is a warning indicating that there is a safety risk in the recycled water and normal use needs to be stopped.

[0102] According to the isolation instruction signal, isolate and introduce the abnormal water body for treatment. The isolation and introduction treatment of the abnormal water body includes cutting off the original water conveyance path, enabling the standby treatment channel, and introducing the abnormal water body into the secondary treatment chamber.

[0103] Specifically, according to the isolation instruction signal, isolate and introduce the abnormal water body for treatment. The isolation and introduction treatment of the abnormal water body includes a series of emergency operations. First, cut off the original water conveyance path, that is, stop conveying this batch of water to the originally scheduled reuse path, which is achieved by closing relevant valves or stopping the pump. Second, enable the standby treatment channel. If there is a standby treatment unit or process, activate it to prepare for the abnormal water body that needs secondary treatment. Finally, introduce the abnormal water body into the secondary treatment chamber. By switching the valves to control the water flow direction, direct this batch of water marked as "abnormal" to a dedicated secondary treatment chamber or temporary storage tank, rather than entering the recycled water tank, to avoid contaminating the existing qualified reclaimed water or being directly used by users.

[0104] Based on the isolation instruction signal, record the abnormal characteristic parameters and reverse-correct the weight distribution of the pollution characteristic matrix.

[0105] Specifically, after receiving the signal, the controller quickly closes the valve leading to the flushing water tank, and at the same time opens the valve leading to the secondary treatment tank, directing this batch of excessive wastewater to the secondary treatment process instead of entering the flushing water tank for direct flushing.

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

[0107] 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 the wastewater and performing synchronization processing to generate optical and electrochemical parameter data;

[0108] A pollution characteristic modeling module, for receiving the optical and electrochemical parameter data, and generating a dynamically updated pollution characteristic matrix through analysis and modeling;

[0109] An intelligent purification control module, configured with a multi-stage membrane treatment unit and a controller, for generating and executing a multi-stage membrane treatment instruction set according to the decision of the pollution characteristic matrix to obtain a hierarchical purification water quality parameter set;

[0110] A reuse path planning module, for performing water quality assessment, path analysis, and priority adjustment according to the hierarchical purification water quality parameter set and related information, and generating a circulating water scheduling path allocation plan;

[0111] The intelligent water network execution and guarantee module connects the sensors and actuators of the water network system, executes the dynamic balance optimization of the water network according to the circulating water scheduling path allocation scheme to generate water volume allocation instructions, and realizes the controlled, safe recycling of reclaimed water and abnormal response processing in combination with the real-time water quality safety monitoring results.

[0112] It should be noted that the functional division and information interaction among the above-mentioned modules are logical. Physically, they can be integrated on the same software platform or distributedly deployed. Their connections represent data flow and control flow, aiming to jointly achieve the goal of dynamic optimization of building energy consumption of the present invention. The above are only exemplary embodiments of the present invention, and the protection scope of the present invention cannot be limited thereby.

Claims

1. An intelligent purification and recycling method for the wastewater of a motorhome, characterized in that, The method includes: Obtaining a dual-mode detection signal of wastewater, and performing synchronous timing alignment on the dual-mode detection signal of wastewater to generate optical and electrochemical parameter data; Analyzing and modeling based on the optical and electrochemical parameter data to generate a pollution feature matrix, where the pollution feature matrix includes a spectral attenuation rate, an organic matter oxidation degree, and a particle size distribution feature; Making a decision based on the pollution feature matrix and executing a multi-stage membrane treatment instruction set to obtain a hierarchical purification water quality parameter set, where the multi-stage membrane treatment instruction set sets a membrane module switching threshold and catalytic treatment parameters; Analyzing available reuse paths by matching the hierarchical purification water quality parameter set with a preset reuse standard set, and adjusting the priority order of the available reuse paths in combination with pre-collected dynamic environmental parameters to generate a circulating water scheduling path allocation plan; According to the circulating water scheduling path allocation plan, combining the water network status information and water storage requirements obtained in real time to generate a water volume allocation instruction for dynamic balance optimization processing of the water network, and executing the circulating water scheduling path allocation plan and the water volume allocation instruction to control the hierarchical transportation and recycling of reclaimed water.

2. The intelligent purification and recycling method for the wastewater of a motorhome according to claim 1, characterized in that, The generating of the optical and electrochemical parameter data includes: Collecting reflected spectral data with continuously distributed wavelengths through an optical sensor array; Measuring charge transfer impedance data of organic matter in wastewater through a bioelectrochemical sensor; Aligning the reflected spectral data and the charge transfer impedance data on the time axis to generate synchronous timing-aligned optical and electrochemical parameter data.

3. The intelligent purification and recycling method for the wastewater of a motorhome according to claim 2, characterized in that, The generating of the pollution feature matrix includes: Decomposing the reflected spectral data into a scattering component and an absorption component to determine the particle size distribution feature and the spectral attenuation rate; Performing wavelet noise reduction processing on the charge transfer impedance data and matching it with a pollutant feature library to determine the organic matter oxidation degree index; Constructing a three-dimensional data space through the spectral attenuation rate, the organic matter oxidation degree index, and the particle size distribution feature; Based on the three-dimensional data space, using a convolutional neural network to calculate a pollution weight factor to generate a dynamically updated pollution feature matrix.

4. A method for intelligent purification and recycling of waste water in a motorhome according to claim 3, characterized in that, The making a decision based on the pollution feature matrix and executing the multi-stage membrane treatment instruction set includes: Determining the catalytic treatment parameters according to the organic matter oxidation degree index; Determining the membrane module switching threshold and related operation parameters according to the particle size distribution feature and the chemical residue characteristics in the pollution feature matrix; Combining the catalytic treatment parameters, the membrane module switching threshold, and the related operation parameters to generate a multi-stage membrane treatment instruction set.

5. The intelligent purification and recycling method for the wastewater of a motorhome according to claim 4, characterized in that, The making a decision based on the pollution feature matrix and executing the multi-stage membrane treatment instruction set further includes: Matching and selecting a chemical agent formula from a preset atomized chemical agent formula library according to the chemical residue characteristics reflected in the pollution feature matrix; Dispersing the chemical agent formula into micron-sized particles through a magnetic drive atomizing nozzle and injecting them into the water to be treated, and dynamically correcting the contact reaction time between the chemical agent formula and the wastewater by using the real-time change rate of the online oxidation-reduction potential monitoring value.

6. The intelligent purification and recycling method for the wastewater of a motorhome according to claim 1, characterized in that, The generating of the circulating water scheduling path allocation plan includes: Mapping the hierarchical purification water quality parameter set to preset water quality grades of Class I, Class II, and Class III; Determine the available reuse paths based on the water quality level and the preset path mapping relationship between each level and the reuse system; Adjust the priority sequence according to the available reuse paths and the on-vehicle real-time water demand, and generate a circulating water scheduling path allocation plan.

7. A method for intelligent purification and recycling of waste water in a motorhome according to claim 6, characterized in that, The adjusting the priority sequence according to the available reuse paths and the on-vehicle real-time water demand includes: When it is detected that the external environmental temperature exceeds the preset high temperature threshold, the priority of the greening irrigation path is increased; When it is detected that the remaining capacity of the clean water tank is lower than the preset safety threshold, the priority of the drinking water replenishment path is increased; When it is detected that the vehicle is in a parked state and there is a demand for bathroom water, the first priority is assigned to the water supply path of the bathroom system.

8. A method for intelligent purification and recycling of waste water in a motor home according to claim 1, characterized in that, The generating the water volume allocation instruction for the dynamic balance optimization of the water network includes: Collect the system state matrix including the capacity of each water tank, pipeline pressure, and pump energy consumption in real time; Based on the system state matrix, use the preset reinforcement learning model to optimize the water volume exchange sequence and value between adjacent water tanks to obtain the basic water volume exchange strategy; Predict the future water use fluctuation trend according to the historical water use data in the preset historical database, and generate a pre-adjustment and storage control instruction; Fuse the basic water volume exchange strategy and the pre-adjustment and storage control instruction to generate a water volume allocation instruction for the dynamic balance optimization of the water network.

9. A method for intelligent purification and recycling of wastewater in a recreational vehicle according to claim 1, characterized in that, The according to the circulating water scheduling path allocation plan further includes: Continuously monitor the hierarchical purification water quality parameters. When any of the monitored hierarchical purification water quality parameter sets exceeds the preset safety range, generate an isolation instruction signal; According to the isolation instruction signal, perform the isolation and import of the abnormal water body. The isolation and import of the abnormal water body includes cutting off the original water transmission path, enabling the standby treatment channel, and importing the abnormal water body into the secondary treatment chamber; Based on the isolation instruction signal, record the abnormal characteristic parameters and reverse-correct the weight allocation of the pollution characteristic matrix.

10. An intelligent purification and recycling system for RV wastewater, which is applied to execute the intelligent purification and recycling method for RV wastewater according to any one of claims 1 to 9, characterized in that, The system includes: A dual-mode sensing and data fusion module, configured with an optical sensor array and a bioelectrochemical sensor, for obtaining dual-mode detection signals of wastewater and performing synchronization processing to generate optical and electrochemical parameter data; A pollution characteristic modeling module, for receiving the optical and electrochemical parameter data and generating a dynamically updated pollution characteristic matrix through analysis and modeling; An intelligent purification control module, configured with a multi-stage membrane treatment unit and a controller, for generating and executing a multi-stage membrane treatment instruction set according to the decision of the pollution characteristic matrix to obtain a hierarchical purification water quality parameter set; A reuse path planning module, for performing water quality assessment, path analysis and priority adjustment according to the hierarchical purification water quality parameter set and related information, and generating a circulating water scheduling path allocation plan; A smart water network execution and guarantee module, connecting the water network system sensors and actuators, performing dynamic balance optimization of the water network according to the circulating water scheduling path allocation plan to generate a water volume allocation instruction, and realizing the controlled, safe recycling and abnormal response processing of reclaimed water in combination with the real-time water quality safety monitoring results.

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