Cloud big data processing method of water quality analysis platform

Abstract

The invention relates to the technical field of water quality analysis, in particular to a cloud big data processing method of a water quality analysis platform. The specific implementation process comprises the following steps: calling a photoelectric sensor group to collect an original water quality signal, performing timestamp mapping on the signal and a geographic space grid, and packaging the signal and the geographic space grid into a water quality information body; resolving a reaction kinetics constraint relationship through a stoichiometric consistency logic gate, repairing data holes by utilizing Kriging space interpolation, and integrating into a water quality analysis set; calculating a nonlinear coupling coefficient between the physical and chemical factors and the environmental flux, constructing a water quality coupling matrix, and generating a water quality ecological tensor through a stoichiometric self-encoding mechanism; and analyzing water quality chemical components based on pedigree homology, and inverting a physicochemical index trajectory by using a time sequence evolution prediction model. By introducing chemical mechanism constraint and environment coupling analysis, the islanding effect of single monitoring data is broken, and rapid analysis, accurate traceability and dynamic trend prediction of water quality data are realized.

Description

Technical Field

[0001] This invention relates to the field of water quality analysis technology, specifically to a cloud-based big data processing method for a water quality analysis platform. Background Technology

[0002] In the field of water quality analysis in chemical plants, existing technologies typically employ sensors based on electrochemical, spectroscopic, or fluorescence methods as front-end sensing units to measure key physicochemical indicators in water bodies in real time, such as hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. These sensors convert the collected analog signals into digital signals and transmit them to a cloud server via a wireless communication network. The current cloud processing workflow primarily involves receiving the data packets uploaded by the sensors, extracting the specific monitoring values ​​through a parsing protocol, directly storing them in a relational database, and comparing them against industry standard thresholds. When the monitored values ​​exceed the set threshold, an alarm signal is generated, and a visual interface displays the curves of water quality parameters changing over time, thus completing the basic analysis and monitoring of the water sample.

[0003] However, existing technologies have inherent limitations in practical applications. Due to the complexity of the water environment, smart sensors are often affected by electromagnetic interference, fluid turbulence, or equipment noise, resulting in abnormal fluctuations. Existing technologies often treat these noisy, missing, or duplicated data as valid analytical samples and directly input them into the database. This leads to biases in the subsequent analysis reports, which fail to accurately reflect the objective situation of the water's chemical properties. Furthermore, they lack the ability to perform cloud-based fusion analysis of strong chemical correlations between different water quality parameters, as well as multi-source heterogeneous data such as meteorological and hydrological data. In summary, existing technologies are limited to independent judgments of single monitoring points or single physicochemical parameters, making it difficult to reveal the deeper patterns of water quality changes through cross-verification of multi-dimensional data. This results in reduced real-time data analysis and an inability to meet the water quality analysis needs of sudden pollution events.

[0004] To address this, a cloud-based big data processing method for a water quality analysis platform is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a cloud-based big data processing method for a water quality analysis platform, enabling cloud-based big data processing for water quality analysis platforms within chemical plants.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A cloud-based big data processing method for a water quality analysis platform includes: The system uses a group of photoelectric sensors to collect raw water quality signals from the chemical plant, including hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. The raw water quality signals are then mapped to a geospatial grid using timestamps and encapsulated into a water quality information body. The reaction kinetic constraints of the water quality information body are calculated in real time using a stoichiometric consistency logic gate, and Kriging space interpolation is used to repair data gaps within the water quality information body, integrating them into a water quality analysis set. The nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set are calculated, and a water quality coupling matrix is ​​constructed. The water quality coupling matrix is ​​mapped to the water quality feature space, and the features are reconstructed and dimensionally reduced by a stoichiometric autoencoder mechanism to generate a water quality ecological tensor that characterizes the current chemical state and ecological trend of the water body. The real-time generated water quality ecological tensor is projected onto a cloud-based pollution spectrum database for intensity calculation, and the water quality chemical composition is analyzed based on spectral homology. Simultaneously, the water quality ecological tensor is input into a time-series evolution prediction model to inversely extrapolate the future physicochemical trajectory of the water body and output a water quality analysis report.

[0007] Preferably, the specific implementation process of using a photoelectric sensor group to collect raw water quality signals containing hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity within the chemical plant, and then mapping the raw water quality signals to a geospatial grid using timestamps and encapsulating them into a water quality information body includes: The system utilizes a photoelectric sensor array to collect simulated light intensity response signals for hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. A multi-channel analog-to-digital converter transforms these simulated light intensity response signals into digital level sequences. A spectral adaptive filtering algorithm is then used to denoise the digital level sequences, eliminating dark current drift and environmental stray light interference, and extracting pure spectral feature values. The spatial vector coordinates of the sampling section and the timestamp of the sampling time are obtained, and the spatial vector coordinates are converted into a geospatial grid index using a spatial gridding algorithm. Based on a spatiotemporal association protocol, the pure spectral feature values ​​are bound to the geospatial grid index and timestamp using metadata, encapsulating them into a water quality information body.

[0008] Preferably, the specific implementation process of solving the reaction kinetic constraints of the water quality information body in real time through stoichiometric consistency logic gates, and using Kriging space interpolation to repair data gaps in the water quality information body and integrate it into a water quality analytical set includes: The water quality information body is imported into a stoichiometric consistency logic gate with a built-in stoichiometric matrix and reaction rate coefficients. A multidimensional constraint algorithm is used to compare the reaction kinetic constraint relationships between parameters in the water quality information body in real time, automatically identifying outlier data that violate the chemical reaction mechanism and marking them as data gaps. For the data gaps, a semi-variogram model based on a geospatial grid is constructed to analyze the anisotropic characteristics of each parameter in spatial distribution and calculate the spatial covariance matrix. The spatial weight coefficients of each neighborhood sampling point on the spatial covariance matrix are solved according to the minimum variance unbiased estimation criterion. Ordinary kriging interpolation is performed to reconstruct the missing physicochemical property values ​​in the data gaps, and the integrated output is a water quality analysis set.

[0009] Preferably, the specific implementation process for calculating the nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set, and constructing the water quality coupling matrix, includes: The water quality analysis set is decomposed to obtain a multidimensional physicochemical factor time series; the hydrological flow field data and meteorological parameters of the sampling area are correlated to obtain an environmental flux vector; a nonlinear mapping model based on kernel function is constructed to project the multidimensional physicochemical factor time series and the environmental flux vector onto the feature space; the maximum mutual information coefficient algorithm is used to deeply mine the statistical dependence between the two in the time domain fluctuations; the nonlinear coupling coefficients characterizing the response intensity of physicochemical indicators to environmental elements are calculated one by one; an orthogonal coordinate index containing the physicochemical factor dimension and the environmental flux dimension is established; according to the logical attributes of each nonlinear coupling coefficient, it is mapped to the corresponding coordinate node; the dimensional differences are eliminated through matrix regularization; and it is encapsulated into the water quality coupling matrix.

[0010] Preferably, the specific implementation process of mapping the water quality coupling matrix to the water quality feature space, and performing feature reconstruction and dimensionality reduction compression through a stoichiometric autoencoding mechanism to generate a water quality ecological tensor characterizing the current chemical state and ecological trend of the water body includes: The water quality coupling matrix is ​​projected onto the water quality feature space using a manifold learning algorithm, preserving the topological neighborhood structure between data. A stoichiometric autoencoder mechanism is then activated, performing convolution operations and nonlinear mapping on the feature space data through the encoder layer to extract water quality variables, remove environmental background noise, and complete data dimensionality reduction and compression. Simultaneously, the decoder layer is driven to reconstruct the features of the water quality variables, calibrating the accuracy of feature extraction based on the criterion of minimizing reconstruction error, and locking the optimal feature vector representing the water body attributes. According to the multidimensional logic of time, space, and chemical attributes, the optimal feature vector is tensor-encapsulated to generate a water quality ecological tensor that reflects the chemical state and ecological trends of the water body.

[0011] Preferably, the specific implementation process of projecting the real-time generated water quality ecological tensor onto a cloud-based pollution spectrum library for intensity calculation, and analyzing the water quality chemical composition based on spectral homology, includes: A cloud-based data channel is established and a pollution spectrum library is loaded. Water body spectral vectors containing industrial wastewater, domestic sewage, and agricultural non-point source pollution are extracted. The water quality ecological tensor is used as a query probe input vector to retrieve the space. Inner product projection operations are performed on the spectral vectors of each water body to quantify and analyze the correlation strength values ​​between the two in the feature dimensions. A pollution source spectrum clustering tree is constructed based on the correlation strength values. A fuzzy pattern recognition algorithm is applied to analyze the belonging weight of the water quality ecological tensor at each branch node of the clustering tree and calculate the spectrum homology probability distribution. The spectrum terminal node corresponding to the maximum probability value is retrieved, and the water quality chemical composition is analyzed and output.

[0012] Preferably, the specific implementation process of simultaneously inputting the water quality ecological tensor into the time-series evolution prediction model to inversely extrapolate the future physicochemical index trajectory of the water body and output a water quality analysis report includes: A historical state buffer pool based on a sliding window mechanism is constructed, and real-time input water quality ecological tensors are stored sequentially to form a continuous temporal feature sequence, which is then imported into a temporal evolution prediction model. The temporal evolution prediction model uses a memory gating mechanism to extract dependent features and fluctuation components in the water quality evolution process, and outputs a hidden layer prediction vector representing the future water body state through nonlinear weighted operations. Using a physical quantity inverse mapping algorithm, the hidden layer prediction vector is back-projected from the feature space to the physicochemical factor dimension space, and the future numerical sequences of various indicators, including hydrogen ion concentration and dissolved oxygen, are calculated one by one. Combined with Bayesian inference logic, the probability distribution density and confidence interval boundaries of the future numerical sequences are calculated, and the evolution trajectory of water body physicochemical indicators, including the error range, is plotted. A report generation engine is triggered to capture the pollution emission source type determination results and evolution trajectory data, and a visualization rendering component is called to generate dynamic trend charts. The chart data and text conclusions are mixed and encapsulated according to industry standard templates to output a water quality analysis report.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention removes environmental noise through a spectral adaptive filtering algorithm and utilizes stoichiometric consistency logic gates and Kriging space interpolation techniques to solve reaction kinetic constraints in real time, automatically identifying and repairing data gaps and outliers. This not only eliminates dark current drift and stray light interference but also completes missing data based on chemical mechanisms, ensuring the purity and objectivity of subsequent sample analyses.

[0014] 2. This invention constructs a water quality coupling matrix by calculating the nonlinear coupling coefficient between physicochemical factors and environmental fluxes (hydrological and meteorological), and then uses a stoichiometric self-encoding mechanism for feature reconstruction. This method breaks down data silos and deeply mines the statistical dependencies between different water quality parameters and environmental elements in the temporal domain fluctuations, thereby revealing more realistic underlying patterns and ecological trends in water quality changes.

[0015] 3. This invention, through phylogenetic homology determination and combined with a time-series evolution prediction model, can inversely predict the future physicochemical trajectory of water quality. It can meet the needs for rapid response, qualitative analysis, and dynamic monitoring of sudden pollution events, and output water quality analysis reports with guiding significance. Attached Figure Description

[0016] Figure 1 This is a flowchart of a cloud-based big data processing method for a water quality analysis platform proposed in this invention. Figure 2 This is a schematic diagram of the stoichiometric self-encoding mechanism proposed in this invention; Figure 3 This is a schematic diagram of the time-series evolution prediction model proposed in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It must be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to constitute any limitation on the scope of protection of this invention. Therefore, all equivalent changes or modifications conceived by those skilled in the art based on the content disclosed in this invention without inventive effort should fall within the scope of protection claimed by this invention.

[0018] Reference Figures 1 to 3 This invention provides a cloud-based big data processing method for a water quality analysis platform, the technical solution of which is as follows: Example 1: Reference Figure 1 This embodiment proposes a cloud-based big data processing method for a water quality analysis platform, including: The system uses a group of photoelectric sensors to collect raw water quality signals from the chemical plant, including hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. The raw water quality signals are then mapped to a geospatial grid using timestamps and encapsulated into a water quality information body. The reaction kinetic constraints of the water quality information body are calculated in real time using a stoichiometric consistency logic gate, and Kriging space interpolation is used to repair data gaps within the water quality information body, integrating them into a water quality analysis set. The nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set are calculated, and a water quality coupling matrix is ​​constructed. The water quality coupling matrix is ​​mapped to the water quality feature space, and the features are reconstructed and dimensionally reduced by a stoichiometric autoencoder mechanism to generate a water quality ecological tensor that characterizes the current chemical state and ecological trend of the water body. The real-time generated water quality ecological tensor is projected onto a cloud-based pollution spectrum database for intensity calculation, and the water quality chemical composition is analyzed based on spectral homology. Simultaneously, the water quality ecological tensor is input into a time-series evolution prediction model to inversely extrapolate the future physicochemical trajectory of the water body and output a water quality analysis report.

[0019] Furthermore, the specific implementation process of using photoelectric sensor arrays to collect raw water quality signals containing hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity within the chemical plant, and then mapping these raw water quality signals to a geospatial grid using timestamps and encapsulating them into a water quality information body includes: The system utilizes a photoelectric sensor array to collect simulated light intensity response signals for hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. A multi-channel analog-to-digital converter transforms these simulated light intensity response signals into digital level sequences. A spectral adaptive filtering algorithm is then used to denoise the digital level sequences, eliminating dark current drift and environmental stray light interference, and extracting pure spectral feature values. The spatial vector coordinates of the sampling section and the timestamp of the sampling time are obtained, and the spatial vector coordinates are converted into a geospatial grid index using a spatial gridding algorithm. Based on a spatiotemporal association protocol, the pure spectral feature values ​​are bound to the geospatial grid index and timestamp using metadata, encapsulating them into a water quality information body.

[0020] Specifically, a photoelectric sensor array is activated to synchronously sense multiple parameters of the water body. This array integrates high-sensitivity photodiodes and narrowband filters. For five key indicators commonly found in chemical plant wastewater discharge—hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity—specific wavelength light sources are excited, and the transmitted or scattered analog light intensity response signals are received. A multi-channel, high-precision analog-to-digital converter converts the continuous analog voltage signal output by the sensors into a discrete digital level sequence at a set sampling frequency. For example, a 24-bit resolution analog-to-digital converter converts microvolt-level light intensity changes into digital quantized values. To address electromagnetic interference and illumination variations in the environment, a spectral adaptive filtering algorithm is used to denoise the digital level sequence. This algorithm, based on the time-frequency characteristics of the signal, constructs a dynamic noise model to track and cancel dark current drift caused by device thermal noise and stray light interference from external illumination changes in real time. In a preferred embodiment, the original signal exhibits nonlinear high-frequency spike noise in the turbidity channel due to strong midday sunlight, and the baseline drifts by 0.2 volts. After processing by the spectral adaptive filtering algorithm, the signal-to-noise ratio is successfully improved by 15 dB, and the superimposed background noise is eliminated, thereby extracting pure spectral feature values ​​that are only related to the concentration of substances in the water.

[0021] The built-in BeiDou / GPS dual-mode positioning module acquires the spatial vector coordinates (longitude, latitude, and elevation) of the current sampling section and simultaneously records the sampling time timestamp accurate to milliseconds. To address the inefficiency of traditional latitude and longitude coordinates in cloud-based big data retrieval, a spatial gridding algorithm is used to convert the spatial vector coordinates into a geospatial grid index. This algorithm, based on the geometric projection rules of the Earth's surface, divides continuous geographic space into hierarchical discrete grid units. For example, the coordinates at longitude 120.456 degrees and latitude 31.234 degrees are mapped to the unique coded index "G-31234-120456" in the L12 level grid. According to a preset spatiotemporal association protocol, the extracted pure spectral feature values ​​are bound to the generated geospatial grid index and timestamp using metadata. This process constructs a standardized data frame structure, tightly coupling and encapsulating measured data such as hydrogen ion concentration (pH 7.2), dissolved oxygen (5.4 mg / L), chemical oxygen demand (45 mg / L), ammonia nitrogen (1.2 mg / L), and turbidity (12 NTU) with spatiotemporal tags to form a water quality information body with a unique identifier, providing standardized data input for subsequent cloud-based big data processing.

[0022] This embodiment effectively improves the problem of low accuracy in water quality monitoring data due to interference from ambient light and equipment temperature drift by forming a water quality information body. At the same time, through geospatial grid processing, it significantly improves the efficiency of spatiotemporal retrieval and correlation analysis of large amounts of water quality data in the cloud, realizing high-precision, gridded monitoring of the complex water environment of chemical industrial parks.

[0023] Furthermore, the specific implementation process of using stoichiometric consistency logic gates to solve the reaction kinetic constraints of the water quality information body in real time, and using Kriging space interpolation to repair data gaps within the water quality information body and integrate it into a water quality analytical set includes: The water quality information body is imported into a stoichiometric consistency logic gate with a built-in stoichiometric matrix and reaction rate coefficients. A multidimensional constraint algorithm is used to compare the reaction kinetic constraint relationships between parameters in the water quality information body in real time, automatically identifying outlier data that violate the chemical reaction mechanism and marking them as data gaps. For the data gaps, a semi-variogram model based on a geospatial grid is constructed to analyze the anisotropic characteristics of each parameter in spatial distribution and calculate the spatial covariance matrix. The spatial weight coefficients of each neighborhood sampling point on the spatial covariance matrix are solved according to the minimum variance unbiased estimation criterion. Ordinary kriging interpolation is performed to reconstruct the missing physicochemical property values ​​in the data gaps, and the integrated output is a water quality analysis set.

[0024] Specifically, the water quality information body containing data on hydrogen ion concentration, dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen, and turbidity is imported into a cloud processing unit. This unit is pre-configured with a stoichiometric consistency logic gate. This stoichiometric consistency logic gate is an integrated model of various intrinsic correlations within multidimensional chemical reaction mechanisms. It internally stores stoichiometric matrices and reaction rate coefficients specific to the wastewater type from the chemical plant, such as the stoichiometric ratio between COD consumption and dissolved oxygen reduction during aerobic degradation of organic matter, and the contribution rate of ammonia nitrogen nitrification to pH (hydrogen ion concentration). A multidimensional constraint algorithm is used to scan and compare in real time whether the numerical relationships between parameters within the current water quality information body conform to reaction kinetic constraints. This multidimensional constraint algorithm is based on significant error detection and is implemented by constructing a balance residual vector R. Its specific logic is as follows: an m×n stoichiometric matrix M (where n is the number of physicochemical factors) is constructed based on m chemical reaction equations from the chemical plant. The reaction flux distribution is represented by v, and the actual flux distribution is represented by V. The consistency equation M∙v=0 is constructed using the law of conservation of mass. The residual R=M∙V is calculated in real time. When the L2 norm of R is greater than the reaction rate fluctuation deviation, the data point is determined to deviate from the chemical reaction trajectory and marked as a data hole. When the data uploaded by the sensor group shows that the chemical oxygen demand concentration suddenly increases from 50 mg / L to 180 mg / L at a certain moment, theoretically, according to the built-in reaction kinetic model, the dissolved oxygen in the same water body should show a downward trend along with the oxidation and decomposition of organic pollutants. If the dissolved oxygen reading still remains at a saturated state of 8.5 mg / L at this time, and no obvious abnormality is observed in turbidity, the stoichiometric consistency logic gate will determine that the dissolved oxygen data violates the basic chemical reaction mechanism of "oxygen-consuming degradation of organic matter" and belongs to outlier data. The dissolved oxygen reading is automatically marked as invalid, and a data hole to be repaired is formed at this time point and spatial location.

[0025] To fill the data gap created by the logic gate removal, a geospatial statistics-based repair process was initiated. For the data gap, historical and real-time data from the surrounding area were retrieved to construct a semi-variogram model based on a geospatial grid. This model analyzes the anisotropic characteristics of the spatial distribution of various water quality parameters by calculating the variance changes between sampling point pairs at different lag distances. For example, the diffusion correlation distance of pollutants may reach 50 meters in the downstream direction, but only 5 meters in the perpendicular direction. This directional difference is quantified and used to calculate the spatial covariance matrix. Based on the minimum variance unbiased estimation criterion (i.e., the Kriging condition), the spatial covariance matrix equation is solved to obtain the spatial weight coefficients of the effective sampling points in the neighborhood surrounding the data gap. Assuming there are four effective neighborhood sampling points A, B, C, and D around the data gap, with distances of 2 meters, 5 meters, 8 meters, and 10 meters from the gap, respectively, they are assigned weights of 0.45, 0.30, 0.15, and 0.10 after calculation. Perform the ordinary Kriging interpolation operation and reconstruct the missing physicochemical property values ​​using a weighted linear combination method.

[0026] This embodiment effectively overcomes the data link breakage problem caused by single sensor failure by introducing a dual mechanism of chemical mechanism constraint and spatial statistical repair, significantly improving the physical authenticity and chemical consistency of water quality monitoring data, and laying a reliable data foundation for subsequent accurate analysis.

[0027] Furthermore, the specific implementation process for calculating the nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set, and constructing the water quality coupling matrix, includes: The water quality analysis set is decomposed to obtain a multidimensional physicochemical factor time series; the hydrological flow field data and meteorological parameters of the sampling area are correlated to obtain an environmental flux vector; a nonlinear mapping model based on kernel function is constructed to project the multidimensional physicochemical factor time series and the environmental flux vector onto the feature space; the maximum mutual information coefficient algorithm is used to deeply mine the statistical dependence between the two in the time domain fluctuations; the nonlinear coupling coefficients characterizing the response intensity of physicochemical indicators to environmental elements are calculated one by one; an orthogonal coordinate index containing the physicochemical factor dimension and the environmental flux dimension is established; according to the logical attributes of each nonlinear coupling coefficient, it is mapped to the corresponding coordinate node; the dimensional differences are eliminated through matrix regularization; and it is encapsulated into the water quality coupling matrix.

[0028] Specifically, the water quality analysis set is decomposed over time to extract multidimensional physicochemical factor time series covering the past 24 to 72 hours. For example, continuous numerical sequences of indicators such as hydrogen ion concentration, dissolved oxygen, and chemical oxygen demand are extracted, with one sampling point every 5 minutes. Ultrasonic Doppler current meters and small weather stations deployed in the sampling area are connected via an industrial Ethernet interface to simultaneously acquire hydrological flow field data (such as flow velocity, flow direction, and water level) and meteorological parameters (such as rainfall, temperature, and air pressure). These heterogeneous data are then aligned on the time axis and combined to generate an environmental flux vector.

[0029] To reveal the dynamic response patterns of physicochemical factors under complex environmental conditions, a nonlinear mapping model based on a kernel function is constructed. This model uses a Gaussian radial basis function as the kernel function, and its bandwidth parameter (usually denoted as σ) is determined based on an adaptive cross-validation method. Specifically, K-fold cross-validation (K=5 in this embodiment) is used to divide the processed water quality data and environmental flux data. In each cross-validation iteration, a series of preset bandwidth parameter values ​​are tried (e.g., from 0.1 to 10, with a step size of 0.5). For each bandwidth parameter value, the model learns to map water quality indicators to environmental fluxes on the training set (or vice versa, depending on the specific target), and then uses the validation set to evaluate the accuracy of the mapping (e.g., using mean squared error or mutual information gain as evaluation metrics). Finally, the bandwidth parameter that optimizes the evaluation metrics on the validation set (e.g., maximizes mutual information gain) is selected as the kernel function bandwidth of the model. Based on this, the maximum mutual information coefficient algorithm is used to perform deep mining on the projected high-dimensional data. Since physicochemical factors (such as chemical oxygen demand) are continuously changing data, while environmental fluxes (such as rainfall and flow velocity) often exhibit pulse-like or long-tailed distribution characteristics, the maximum mutual information coefficient algorithm performs rank transformation preprocessing on the two sets of time series data, converting their numerical magnitudes into the data's ranking position in the sequence. This maps the non-uniformly distributed data to a uniformly distributed rank space, eliminating the interference of numerical dimension differences on grid partitioning. When constructing the grid to discretize the dependency relationship, an upper limit constraint is set on the grid partitioning resolution. This upper limit is set to approximately 0.6 times the total number of samples at the current sampling point, thereby limiting the fineness of the grid partitioning and preventing overfitting to random noise due to excessively dense grids. Within the resolution upper limit, a dynamic programming search mechanism is initiated. For the two dimensions of physicochemical factors and environmental fluxes, all possible row and column partitioning combinations are traversed to find the optimal grid partitioning scheme that maximizes the mutual information value. The calculated maximum mutual information value is normalized using the logarithm of the partitioning dimensions, and the output value between 0 and 1 is used as the nonlinear coupling coefficient. An orthogonal coordinate index is established, comprising physicochemical factor dimensions (row index) and environmental flux dimensions (column index). Based on the logical attributes of each nonlinear coupling coefficient, it is mapped to the corresponding coordinate nodes. Considering the large dimensions of different parameters (e.g., flow velocity is in m / s, while turbidity is in NTU), L2 norm normalization is performed on all elements in the matrix to eliminate dimensional differences, and the final result is encapsulated as the water quality coupling matrix. A specific instance of this matrix may be represented as a 5x4 tensor slice, where the value 0.82 at position (2,3) represents the strong coupling characteristic of chemical oxygen demand to flow velocity.

[0030] This implementation method effectively addresses the problem of focusing solely on the numerical changes of water quality parameters while neglecting the impact of environmental context by constructing a water quality coupling matrix. By quantifying the response intensity of physicochemical factors to environmental fluxes, it can distinguish whether water quality fluctuations originate from actual pollution discharge events or normal hydrological and meteorological changes (such as rainfall dilution or sediment disturbance), thereby significantly reducing the false alarm rate and providing deep feature data with environmental context awareness for subsequent pollution source tracing.

[0031] Furthermore, the specific implementation process of mapping the water quality coupling matrix to the water quality feature space, and performing feature reconstruction and dimensionality reduction compression through a stoichiometric autoencoding mechanism to generate a water quality ecological tensor characterizing the current chemical state and ecological trend of the water body includes: The water quality coupling matrix is ​​projected onto the water quality feature space using a manifold learning algorithm, preserving the topological neighborhood structure between data. A stoichiometric autoencoder mechanism is then activated, performing convolution operations and nonlinear mapping on the feature space data through the encoder layer to extract water quality variables, remove environmental background noise, and complete data dimensionality reduction and compression. Simultaneously, the decoder layer is driven to reconstruct the features of the water quality variables, calibrating the accuracy of feature extraction based on the criterion of minimizing reconstruction error, and locking the optimal feature vector representing the water body attributes. According to the multidimensional logic of time, space, and chemical attributes, the optimal feature vector is tensor-encapsulated to generate a water quality ecological tensor that reflects the chemical state and ecological trends of the water body.

[0032] Reference Figure 2 Specifically, after constructing the water quality coupling matrix containing information on the correlation between physicochemical factors and environmental fluxes, the core stage of feature extraction and data reconstruction begins. Since the original water quality coupling matrix often has high-dimensional, nonlinear, and sparse characteristics, directly using it for analysis can easily lead to the "curse of dimensionality." Therefore, it needs to be mapped to a low-dimensional and compact water quality feature space.

[0033] Considering that changes in water quality parameters are often driven by several potential control variables (such as sewage discharge intensity and rainfall dilution ratio), these data are distributed on a low-dimensional manifold in a high-dimensional space. Local linear embedding or t-SNE algorithms are used as specific manifold learning methods to project the high-dimensional water quality coupling matrix onto the water quality feature space. During this process, the algorithm strictly maintains the topological neighborhood structure between data points; that is, two similar water quality state points in the original high-dimensional space (e.g., two moments both impacted by acidic wastewater) remain adjacent in the projected low-dimensional feature space, thus ensuring that the intrinsic geometric structure of the data is not destroyed. Subsequently, the chemometric autoencoder mechanism is activated, an unsupervised learning architecture based on deep neural networks, specifically optimized for chemometric data. The stoichiometric self-encoding mechanism consists of an encoder layer and a decoder layer. The encoder layer receives feature space data, extracts core water quality variables through convolution operations and nonlinear mapping, removes environmental background noise, and performs dimensionality reduction and compression of the data to generate a hidden state vector. The decoder layer performs inverse operations on the hidden state vector to reconstruct features, calibrates the accuracy of feature extraction by comparing the error between the reconstructed data and the original data, and finally locks the optimal feature vector representing the water body attributes. Specifically, the encoder layer receives feature space data after manifold mapping, performs sliding convolution along the time axis using a one-dimensional convolutional neural network (using a 3×3 kernel and a stride of 1), and performs nonlinear mapping through multiple layers of continuous convolution-pooling operations and the ReLU activation function to compress the high-dimensional coupling matrix. At the end of the encoder, a fully connected layer is used to map the feature map into a hidden state vector of length 8, thereby representing the core chemical characteristics of the water body. Specifically, the decoder layer performs the inverse operation of the encoder, using the aforementioned hidden state vector of length 8 to restore the data back to the original feature form of length 128. Based on a minimum reconstruction error criterion (such as mean squared error MSE), the difference between the reconstructed data and the original input is calculated. If the error exceeds a preset threshold (such as 0.01), the network weights are adjusted through backpropagation until the reconstruction error is minimized. At this point, the hidden state vector located in the middle layer of the network is locked as the optimal feature vector representing the water body attributes. Following a multi-dimensional logic of time (monitoring time), space (geographic grid location), and chemical attributes (extracted latent feature dimensions), the locked optimal feature vectors are stacked and recombined in an ordered manner. For example, a third-order tensor with dimensions of [time step T × number of spatial grids N × feature dimension 8] is constructed. This generated object is the water quality ecological tensor.

[0034] This implementation method effectively addresses the challenge of handling complex nonlinear characteristics of water quality data by introducing a stoichiometric autoencoder mechanism and manifold learning. Through a self-calibration process of "compression-reconstruction," pure and highly concentrated ecological characteristics can be extracted from noisy and redundant raw data, significantly reducing the complexity of subsequent computational models and substantially improving the sensitivity and accuracy of identifying subtle pollution trends.

[0035] Furthermore, the specific process of projecting the real-time generated water quality ecological tensor onto a cloud-based pollution spectrum database for intensity calculation, and analyzing the water quality chemical composition based on spectral homology, includes: A cloud-based data channel is established and a pollution spectrum library is loaded. Water body spectral vectors containing industrial wastewater, domestic sewage, and agricultural non-point source pollution are extracted. The water quality ecological tensor is used as a query probe input vector to retrieve the space. Inner product projection operations are performed on the spectral vectors of each water body to quantify and analyze the correlation strength values ​​between the two in the feature dimensions. A pollution source spectrum clustering tree is constructed based on the correlation strength values. A fuzzy pattern recognition algorithm is applied to analyze the belonging weight of the water quality ecological tensor at each branch node of the clustering tree and calculate the spectrum homology probability distribution. The spectrum terminal node corresponding to the maximum probability value is retrieved, and the water quality chemical composition is analyzed and output.

[0036] Specifically, an encrypted cloud data channel is established to load a pre-built pollution spectrum library from a cloud server. This pollution spectrum library is a multi-dimensional vector database trained on massive historical data, storing water body spectral vectors from various typical pollution sources, including industrial wastewater (such as electroplating and dyeing wastewater), domestic sewage (such as urban laundry wastewater and fecal sewage), and agricultural non-point source pollution (such as fertilizer runoff and livestock wastewater). To eliminate generalization errors caused by differences in emission scales from different factories or regional background values, all vectors entered into the database and those to be tested undergo L2 norm normalization, converting absolute concentration values ​​into unit direction vectors representing the proportional structure of pollutant components. This ensures that subsequent inner product operations only reflect the topological similarity of the water quality fingerprint, rather than the emission magnitude.

[0037] The real-time generated water quality ecological tensor is used as the input vector retrieval space for the query probe. Within this space, an inner product projection operation is performed on the spectral vectors of each water body in the database. This operation geometrically calculates the cosine of the angle between the real-time tensor and the standard vectors in the database, thereby quantifying the correlation strength between the two in the feature dimension. To further analyze the water quality chemical composition, a pollution source hierarchy clustering tree is constructed based on the correlation strength values. The root node of this tree structure represents total pollution, the first-level branches are industrial / domestic / agricultural, and the second-level branches are more refined (e.g., the industrial branch is divided into chemical, metallurgical, food processing, etc.). A fuzzy pattern recognition algorithm is applied to analyze the weight of the water quality ecological tensor at each branch node of the clustering tree. The fuzzy pattern recognition algorithm specifically includes: constructing a standard fuzzy pattern library based on the typical feature vectors of each branch node, and treating the water quality ecological tensor to be identified as the sample to be identified; using the generalized weighted Euclidean distance algorithm to calculate the difference distance between the sample to be identified and each standard fuzzy pattern in the feature space, thereby quantifying the degree of separation between the two; introducing Cauchy distribution or semi-trapezoidal distribution as the fuzzy membership function, mapping the calculated difference distance to the original membership value between 0 and 1, following the logic that the smaller the distance, the higher the membership degree; performing a normalization operation on the original membership degree of all child branches under the same parent node to ensure that the sum of the membership weights of each branch is strictly equal to 1, and locking the branch with the largest weight value as the next-level retrieval path according to the principle of maximum membership degree. Based on the above weight chain, calculating the probability distribution of phylogenetic origin, and retrieving the phylogenetic end node corresponding to the maximum probability value in the entire phylogenetic tree.

[0038] This implementation method introduces spectral homology determination and fuzzy pattern recognition technology, and compares abstract water quality data with specific pollution source fingerprints, thus achieving a leap from quantitative monitoring to qualitative source tracing, enabling effective analysis of the chemical composition of water in chemical plants.

[0039] Furthermore, the specific implementation process of simultaneously inputting the water quality ecological tensor into the time-series evolution prediction model to inversely extrapolate the future physicochemical trajectory of the water body and output a water quality analysis report includes: A historical state buffer pool based on a sliding window mechanism is constructed, and real-time input water quality ecological tensors are stored sequentially to form a continuous temporal feature sequence, which is then imported into a temporal evolution prediction model. The temporal evolution prediction model uses a memory gating mechanism to extract dependent features and fluctuation components in the water quality evolution process, and outputs a hidden layer prediction vector representing the future water body state through nonlinear weighted operations. Using a physical quantity inverse mapping algorithm, the hidden layer prediction vector is back-projected from the feature space to the physicochemical factor dimension space, and the future numerical sequences of various indicators, including hydrogen ion concentration and dissolved oxygen, are calculated one by one. Combined with Bayesian inference logic, the probability distribution density and confidence interval boundaries of the future numerical sequences are calculated, and the evolution trajectory of water body physicochemical indicators, including the error range, is plotted. A report generation engine is triggered to capture the pollution emission source type determination results and evolution trajectory data, and a visualization rendering component is called to generate dynamic trend charts. The chart data and text conclusions are mixed and encapsulated according to industry standard templates to output a water quality analysis report.

[0040] Reference Figure 3 Specifically, a historical state buffer pool based on the sliding window mechanism is constructed in the memory of a cloud server. This historical state buffer pool is a first-in, first-out (FIFO) queue structure used to store recently generated water quality ecological tensors. The sliding window duration is set to the past 24 hours, with a time step of 5 minutes; therefore, the buffer pool capacity is set to 288 tensor units. Whenever the system generates a new real-time water quality ecological tensor, this tensor is sequentially stored at the tail of the buffer pool queue, while the oldest tensor at the head of the queue is removed, thus forming a continuous and dynamically updated temporal feature sequence. This sequence is then imported into a pre-trained temporal evolution prediction model.

[0041] The temporal evolution prediction model employs a two-layer stacked Long Short-Term Memory (LSTM) network architecture. The first hidden layer has 128 neurons and enables sequence return mode to capture the long-term dependency features and trends of water quality data. The second hidden layer has 64 neurons to further refine high-dimensional abstract features. A Dropout layer with a dropout rate of 0.2 is embedded between the two layers to prevent overfitting by randomly disconnecting some neuron connections during training. A fully connected layer is connected at the end to accurately map the network output dimension to a hidden layer prediction vector of length eight. In terms of internal gate control logic design, each memory unit contains three core control components: a forget gate, an input gate, and an output gate. The Sigmoid activation function generates values ​​between 0 and 1 as gate signals. The forget gate determines how much of the unit state from the previous time step is retained, the input gate controls how much new information is written to the state at the current time step, and the output gate determines the final output hidden state value. Simultaneously, the hyperbolic tangent activation function is used to process candidate states. Without using complex mathematical formulas, nonlinear transformations are used to achieve smooth processing and feature memorization of extreme values ​​in water quality fluctuations. In terms of model training and optimization, mean squared error is selected as the loss function to quantify the difference between the predicted vector and the actual evolution vector. The Adam adaptive moment estimator optimizer is used for gradient backpropagation and parameter update. The initial learning rate is set to 0.001 and a dynamic decay strategy is configured. At the same time, an early stopping mechanism is introduced. If the error on the validation set does not decrease within ten consecutive training cycles, the training is automatically terminated and the current optimal weight parameters are saved, thereby completing the pre-training and finalization of the model.

[0042] The physical quantity inverse mapping algorithm transforms abstract predictions into readable indicators. The specific process of this algorithm includes: First, constructing a multilayer perceptron neural network as the inverse mapping entity. The input layer has 8 nodes, matching the dimension of the hidden layer prediction vector, and the output layer has 5 nodes, matching the number of water quality physicochemical indicators. A fully connected hidden layer with 30 neurons is placed between the input and output to handle the nonlinear mapping relationship. The input hidden layer prediction vector is weighted and nonlinearly transformed using a rectified linear unit activation function to calculate a normalized numerical sequence within the 0-1 range. Then, the inverse normalization operation logic is executed, calling the historical maximum and minimum values ​​of each water quality indicator from memory. The normalized numerical sequence is multiplied by the data range and the minimum value is added, thus restoring the dimensionless neural network output to concentration values ​​or readings with actual physical units. The future numerical sequences of various indicators, including hydrogen ion concentration, dissolved oxygen, and chemical oxygen demand, are calculated and restored one by one. For example, if the calculated pH value is 6.8 in the first hour, 6.5 in the second hour, and 6.1 in the third hour, it indicates that the water body is about to become acidified. However, since a single numerical prediction lacks risk assessment value, Bayesian inference logic is used to calculate the statistical probability distribution density of the future numerical sequence. This not only provides the predicted mean but also calculates the boundaries of the 95% confidence interval. In this case, for the predicted pH value of 6.1 in the third hour, the calculated confidence interval is [5.9, 6.3], meaning that the indicator has a 95% probability of falling within this range. This allows for the plotting of the water body's physicochemical indicator evolution trajectory, including a gray error band, visually demonstrating the range of prediction uncertainty.

[0043] The report generation engine is triggered to perform automated document synthesis. The engine first retrieves the pollution source type determination result of "acidic electroplating wastewater" from the previous steps, and simultaneously obtains the aforementioned evolution trajectory data with an error range. Then, it calls the visualization rendering component to generate dynamic trend charts, where solid lines represent historical data, dashed lines represent predicted trajectories, and shaded areas represent confidence intervals. Following GB / T or industry standard formatting templates, these chart data are mixed with automatically generated text conclusions (e.g., "Warning: The pH value of the water body is expected to fall below the compliance threshold within the next 3 hours; it is recommended to immediately investigate the pickling workshop") for formatting and encapsulation, ultimately outputting a water quality analysis report in PDF or HTML format.

[0044] This implementation method, by introducing time-series evolution prediction and Bayesian inference, can not only predict the specific trajectory of water quality deterioration in advance, thus gaining golden time for emergency response, but also provide decision-makers with a scientific basis for risk assessment through the calculation of confidence intervals, significantly improving the predictability and safety of water environment management in chemical industrial parks.

[0045] Example 2: This embodiment fully deploys the above-mentioned cloud-based big data processing method for a water quality analysis platform within the X chemical plant's water quality analysis platform to achieve the processing of cloud-based big data.

[0046] Furthermore, the data acquisition and packaging process was initiated, utilizing photoelectric sensor arrays deployed at the wastewater outlets and key process nodes of the X chemical plant to perform high-frequency sampling of the water body. The sensor arrays not only included conventional electrochemical probes but also integrated photoelectric sensor arrays to collect real-time analog light intensity response signals, including hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. A multi-channel analog-to-digital converter was used to convert the weak analog light intensity signals into a 24-bit precision digital level sequence. To eliminate electromagnetic noise generated by the high-frequency inverter in the industrial environment and environmental interference caused by changes in the angle of sunlight, a spectral adaptive filtering algorithm was run to dynamically reduce noise in the digital level sequence, removing baseline drift and stray light components, and extracting pure spectral feature values ​​with a signal-to-noise ratio higher than 30dB. The spatial vector coordinates of the sampling section were obtained through a BeiDou positioning module, and millisecond-level timestamps were recorded. Using a spatial gridding algorithm, physical coordinates (such as 120.5°E, 31.3°N) are mapped to geospatial grid indexes with unique codes (e.g., Grid-ID: X31Y120-Z05). Subsequently, based on a spatiotemporal association protocol, the aforementioned pure spectral characteristics (such as pH=6.5, COD=120mg / L, etc.) are bound to the grid index and timestamp as metadata, encapsulating them to generate a standardized water quality information body.

[0047] Further, the data cleaning and repair phase begins. The generated water quality data is imported into a cloud processing center, which incorporates a stoichiometric consistency logic gate. This gate is pre-configured with a chemical reaction mechanism model for the specific wastewater system of the chemical plant (e.g., the quantitative relationship between alkalinity consumption and pH decrease during nitrification). A multidimensional constraint algorithm is used to compare the reaction kinetic constraints between parameters within the water quality data in real time. When the logic gate detects a sharp drop in ammonia nitrogen concentration at a certain moment, but an abnormal increase in pH and no consumption of dissolved oxygen, it determines that the basic principles of nitrification have been violated. This set of ammonia nitrogen data is then identified as outlier data caused by sensor malfunction and marked as a data gap. To repair this gap, a semi-variogram model based on a geospatial grid is constructed to analyze the spatial correlation and anisotropy characteristics of water quality parameters at each grid point within a 50-meter radius, calculating the spatial covariance matrix. Based on the minimum variance unbiased estimation criterion (Kriging condition), the spatial weight coefficients of the effective sampling points in the neighborhood are calculated. Ordinary Kriging interpolation is performed, and the ammonia nitrogen value at the cavity is reconstructed into a theoretical value that conforms to the diffusion law using the surrounding effective data. The integrated output is a water quality analysis set after cleaning and restoration.

[0048] Furthermore, a multidimensional environmental coupling analysis is performed. The water quality analytical set is decomposed to obtain the time series of each physicochemical factor, and data from current meters and meteorological stations deployed in the sampling area (e.g., flow velocity 0.5 m / s, rainfall 10 mm / h) are synchronously correlated to synthesize an environmental flux vector. To explore the nonlinear impact of environmental factors on water quality changes, a kernel-based nonlinear mapping model is constructed, projecting the physicochemical factor sequences and environmental flux vectors into a high-dimensional feature space. The maximum mutual information coefficient algorithm is used to deeply mine the statistical dependence between the two in the time-domain fluctuations, calculating the nonlinear coupling coefficient characterizing the response intensity of physicochemical indicators to environmental factors and generating a water quality coupling matrix. A manifold learning algorithm is used to project the water quality coupling matrix into a low-dimensional water quality feature space, and a stoichiometric autoencoder mechanism is initiated. A convolutional neural network in the encoder layer performs nonlinear mapping on the feature space data, extracting latent variables that reflect the chemical state of the water body, while removing redundant noise caused by environmental background (e.g., dilution from normal rainfall). The decoder layer is synchronously driven to reconstruct these latent variables, continuously calibrating the network weights based on the criterion of minimizing reconstruction error until the optimal feature vector characterizing the water body properties is locked. Based on the multidimensional logic of time, space and chemical attributes, these optimal feature vectors are tensor-encapsulated to generate a water quality ecological tensor that can dynamically reflect the chemical state and ecological trends of water bodies.

[0049] Furthermore, a cloud-based data channel is established and a pollution spectrum library is loaded. This library pre-stores water body spectral vectors for various standard pollution sources, including electroplating wastewater, domestic sewage, and agricultural runoff. The real-time generated water quality ecological tensor is used as a query probe, and inner product projection operations are performed in the vector retrieval space. Based on the operation results, a pollution source phylogenetic clustering tree is constructed, and a fuzzy pattern recognition algorithm is applied to analyze the weight of the tensor at each branch node of the clustering tree. The water quality ecological tensor is synchronously stored in a historical state buffer pool based on a sliding window mechanism, forming a continuous temporal feature sequence, which is then input into a time-series evolution prediction model. The time-series evolution prediction model uses a memory gating mechanism to extract long-term dependent features (such as the cumulative effect of pollutants) during the water quality evolution process. Through nonlinear weighted operations, it outputs a hidden layer prediction vector representing the future water body state. Using a physical quantity inverse mapping algorithm, this hidden layer vector is back-projected back into the physicochemical factor dimension space to calculate the numerical sequence indicating that the hydrogen ion concentration will further decrease from 6.5 to 4.0 within the next 4 hours, and dissolved oxygen will be depleted. Combining Bayesian inference logic, the 95% confidence interval of the predicted sequence is calculated, and the evolution trajectory including the error range is plotted. The report generation engine is triggered, capturing the source tracing conclusion of "acidic electroplating wastewater" and the predicted trajectory of "pH value about to exceed the standard," calling the visualization rendering component to generate a dynamic trend chart, and automatically formatting it according to an industry standard template, outputting a complete water quality analysis report including source tracing results, trend warnings, and confidence level assessments.

[0050] This embodiment improves the accuracy and completeness of source data through dual verification of chemical analysis and spatial statistics; it constructs a water quality coupling matrix and ecological tensor, deeply integrates environmental flux data, and uses a self-encoding mechanism to remove background noise, achieving accurate extraction of complex water environment characteristics; through the combination of cloud-based feature spectral library and time-series evolution prediction model, it can accurately identify pollution source types and predict future evolution trajectories, providing data support for environmental risk management of chemical plants.

[0051] It should be clarified that the embodiments described above are merely exemplary and are intended to aid in understanding the present invention, not to limit it. Those skilled in the art can make various changes and modifications after grasping the core ideas of the present invention. Therefore, the scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud-based big data processing method for a water quality analysis platform, characterized in that, include: The system uses a group of photoelectric sensors to collect raw water quality signals from the chemical plant, including hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. The raw water quality signals are then mapped to a geospatial grid using timestamps and encapsulated into a water quality information body. The reaction kinetic constraints of the water quality information body are calculated in real time using a stoichiometric consistency logic gate, and Kriging space interpolation is used to repair data gaps within the water quality information body, integrating them into a water quality analysis set. Calculate the nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set, and construct the water quality coupling matrix; The water quality coupling matrix is ​​mapped to the water quality feature space, and the features are reconstructed and dimension-reduced by a stoichiometric autoencoder mechanism to generate a water quality ecological tensor that characterizes the current chemical state and ecological trend of the water body. The real-time generated water quality ecological tensor is projected onto a cloud-based pollution spectrum database for intensity calculation, and the water quality chemical composition is analyzed based on spectral homology. Simultaneously, the water quality ecological tensor is input into a time-series evolution prediction model to inversely extrapolate the future physicochemical trajectory of the water body and output a water quality analysis report.

2. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process of using a photoelectric sensor array to collect raw water quality signals containing hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity within a chemical plant, and then mapping these raw water quality signals to a geospatial grid using timestamps and encapsulating them into a water quality information body includes: The system utilizes a photoelectric sensor array to collect simulated light intensity response signals for hydrogen ion concentration, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, and turbidity. A multi-channel analog-to-digital converter transforms these simulated light intensity response signals into digital level sequences. A spectral adaptive filtering algorithm is then used to denoise the digital level sequences, eliminating dark current drift and environmental stray light interference, and extracting pure spectral feature values. The spatial vector coordinates of the sampling section and the timestamp of the sampling time are obtained, and the spatial vector coordinates are converted into a geospatial grid index using a spatial gridding algorithm. Based on a spatiotemporal association protocol, the pure spectral feature values ​​are bound to the geospatial grid index and timestamp using metadata, encapsulating them into a water quality information body.

3. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process of integrating the reaction kinetic constraints of the water quality information body into a water quality analytical set by using stoichiometric consistency logic gates to solve the data gaps within the water quality information body and using Kriging space interpolation to repair the data gaps within the water quality information body includes: The water quality information body is imported into a stoichiometric consistency logic gate with a built-in stoichiometric matrix and reaction rate coefficients. A multidimensional constraint algorithm is used to compare the reaction kinetic constraint relationships between parameters in the water quality information body in real time, automatically identifying outlier data that violate the chemical reaction mechanism and marking them as data gaps. For the data gaps, a semi-variogram model based on a geospatial grid is constructed to analyze the anisotropic characteristics of each parameter in spatial distribution and calculate the spatial covariance matrix. The spatial weight coefficients of each neighborhood sampling point on the spatial covariance matrix are solved according to the minimum variance unbiased estimation criterion. Ordinary kriging interpolation is performed to reconstruct the missing physicochemical property values ​​in the data gaps, and the integrated output is a water quality analysis set.

4. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process for calculating the nonlinear coupling coefficients between each physicochemical factor and environmental flux in the water quality analysis set, and constructing the water quality coupling matrix, includes: The water quality analysis set is decomposed to obtain a multidimensional physicochemical factor time series; the hydrological flow field data and meteorological parameters of the sampling area are correlated to obtain an environmental flux vector; a nonlinear mapping model based on kernel function is constructed to project the multidimensional physicochemical factor time series and the environmental flux vector onto the feature space; the maximum mutual information coefficient algorithm is used to deeply mine the statistical dependence between the two in the time domain fluctuations; the nonlinear coupling coefficients characterizing the response intensity of physicochemical indicators to environmental elements are calculated one by one; an orthogonal coordinate index containing the physicochemical factor dimension and the environmental flux dimension is established; according to the logical attributes of each nonlinear coupling coefficient, it is mapped to the corresponding coordinate node; the dimensional differences are eliminated through matrix regularization; and it is encapsulated into the water quality coupling matrix.

5. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process of mapping the water quality coupling matrix to the water quality feature space, and reconstructing and compressing features through a stoichiometric autoencoder mechanism to generate a water quality ecological tensor characterizing the current chemical state and ecological trend of the water body includes: The water quality coupling matrix is ​​projected onto the water quality feature space using a manifold learning algorithm, preserving the topological neighborhood structure between data. A stoichiometric autoencoder mechanism is then activated, performing convolution operations and nonlinear mapping on the feature space data through the encoder layer to extract water quality variables, remove environmental background noise, and complete data dimensionality reduction and compression. Simultaneously, the decoder layer is driven to reconstruct the features of the water quality variables, calibrating the accuracy of feature extraction based on the criterion of minimizing reconstruction error, and locking the optimal feature vector representing the water body attributes. According to the multidimensional logic of time, space, and chemical attributes, the optimal feature vector is tensor-encapsulated to generate a water quality ecological tensor that reflects the chemical state and ecological trends of the water body.

6. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process of projecting the real-time generated water quality ecological tensor onto a cloud-based pollution spectrum database for intensity calculation, and analyzing the water quality chemical composition based on spectral homology, includes: A cloud-based data channel is established and a pollution spectrum library is loaded. Water body spectral vectors containing industrial wastewater, domestic sewage, and agricultural non-point source pollution are extracted. The water quality ecological tensor is used as a query probe input vector to retrieve the space. Inner product projection operations are performed on the spectral vectors of each water body to quantify and analyze the correlation strength values ​​between the two in the feature dimensions. A pollution source spectrum clustering tree is constructed based on the correlation strength values. A fuzzy pattern recognition algorithm is applied to analyze the belonging weight of the water quality ecological tensor at each branch node of the clustering tree and calculate the spectrum homology probability distribution. The spectrum terminal node corresponding to the maximum probability value is retrieved, and the water quality chemical composition is analyzed and output.

7. The cloud-based big data processing method for a water quality analysis platform according to claim 1, characterized in that, The specific implementation process of simultaneously inputting the water quality ecological tensor into the time-series evolution prediction model, inverting and extrapolating the future physicochemical trajectory of the water body, and outputting a water quality analysis report includes: A historical state buffer pool based on a sliding window mechanism is constructed, and real-time input water quality ecological tensors are stored sequentially to form a continuous temporal feature sequence, which is then imported into a temporal evolution prediction model. The temporal evolution prediction model uses a memory gating mechanism to extract dependent features and fluctuation components in the water quality evolution process, and outputs a hidden layer prediction vector representing the future water body state through nonlinear weighted operations. Using a physical quantity inverse mapping algorithm, the hidden layer prediction vector is back-projected from the feature space to the physicochemical factor dimension space, and the future numerical sequences of various indicators, including hydrogen ion concentration and dissolved oxygen, are calculated one by one. Combined with Bayesian inference logic, the probability distribution density and confidence interval boundaries of the future numerical sequences are calculated, and the evolution trajectory of water body physicochemical indicators, including the error range, is plotted. A report generation engine is triggered to capture the pollution emission source type determination results and evolution trajectory data, and a visualization rendering component is called to generate dynamic trend charts. The chart data and text conclusions are mixed and encapsulated according to industry standard templates to output a water quality analysis report.

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