Quantum sensing technology-driven water quality pollution rapid detection method

Abstract

The invention relates to the technical field of water quality pollution detection, in particular to a water quality pollution rapid detection method driven by a quantum sensing technology. A quantum sensor array is deployed in a water area to be detected, and quantum signal response data of the water area is collected; constructing a pollutant quantum recognition model, and inputting quantum signal response data into the model to obtain an initial predicted value of pollutant concentration; generating an initial detection parameter of the quantum detection instrument based on the initial predicted value, executing primary pollution detection by the instrument according to the parameter, and collecting primary deviation data of an actual detection value and an expected detection value; determining a detection error according to the deviation data, further correcting the initial detection parameter, and generating an optimized detection parameter; and finally, the instrument performs secondary pollution detection based on the optimized detection parameters. According to the method, complex water sample pretreatment is not needed, water quality pollution detection can be rapidly and accurately completed, detection requirements of different water areas are met, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of water pollution detection technology, and in particular to a rapid water pollution detection method driven by quantum sensing technology. Background Technology

[0002] In the current water environment monitoring system, water pollution detection technology is a crucial means of ensuring water resource security, widely applied in various scenarios such as drinking water safety monitoring, industrial wastewater discharge supervision, and natural water body ecological protection. Traditional water pollution detection methods largely rely on laboratory analytical techniques, such as chromatography, mass spectrometry, and spectrophotometry. These methods typically require on-site water sample collection, followed by transportation to a specialized laboratory. After a complex pretreatment process, large analytical instruments are used to determine the pollutant concentration. However, this type of detection method has significant limitations. During collection, transportation, and storage, water samples are easily affected by external factors such as temperature, light, and oxygen, leading to changes in pollutant composition and thus affecting the accuracy and reliability of the test results. Laboratory analysis procedures are cumbersome, with a single test often taking several hours or even days to complete, making it difficult to meet the needs of scenarios requiring high detection speed, such as emergency monitoring of sudden water pollution events and large-scale dynamic monitoring of water bodies. With technological advancements, rapid on-site detection technologies have emerged, such as portable detection devices based on electrochemical and optical sensors. While these devices enable real-time on-site detection and shorten testing time, their sensitivity and specificity remain insufficient due to limitations in the sensor's inherent detection principle. For low-concentration, complex pollutants, conventional sensors are easily interfered with by other coexisting substances in the water, leading to significant detection errors and hindering accurate identification and quantification of pollutants. Furthermore, most existing on-site detection devices employ fixed detection parameters, making dynamic adjustments based on the specific water quality characteristics of different bodies of water difficult, further reducing the applicability and accuracy of the test results. In practical applications, conventional detection methods often require multiple adjustments to the detection plan and repeated verification when dealing with water bodies of varying pollution levels and pollutant types. This not only increases detection costs and workload but also hinders rapid and accurate assessment of water pollution conditions, failing to provide timely and effective technical support for water pollution control decisions. Summary of the Invention

[0003] The main objective of this invention is to provide a rapid detection method for water pollution driven by quantum sensing technology, aiming to solve the technical problems in the prior art.

[0004] This invention proposes a rapid water pollution detection method driven by quantum sensing technology, comprising: A quantum sensor array is deployed in the water area to be tested, and quantum signal response data in the water area is collected; A quantum identification model for pollutants in the water area to be detected is constructed, and the quantum signal response data is input into the quantum identification model to obtain an initial predicted value of pollutant concentration in the water area to be detected. Initial detection parameters for the quantum detection instrument are generated based on the initial predicted values ​​of the pollutant concentration. After the quantum detection instrument performs the initial pollution detection action based on the initial detection parameters, it collects the first deviation data between the actual detection value and the expected detection value. The detection error is determined based on the initial deviation data between the actual detection value and the expected detection value, and the initial detection parameters are corrected based on the detection error to generate optimized detection parameters for the quantum detection instrument. The quantum detection instrument performs secondary pollution detection based on the optimized detection parameters.

[0005] Preferably, the process of deploying a quantum sensor array in the water area to be detected and collecting quantum signal response data in the water area to be detected includes: Multiple sets of quantum excitation probes are deployed in the water area to be tested, and the quantum state transition response sequence of the water area to be tested is collected within a fixed monitoring period; Analyze the water flow characteristics and potential pollution source distribution of the water body to be tested, and obtain the spectral interference weights of different pollution sources; Based on the quantum state transition response sequence and the spectral interference weights of different pollution sources, a quantum signal propagation topology model of the water area to be tested is constructed, and quantum signal response data of the water area to be tested is extracted according to time nodes.

[0006] Preferably, the step of constructing a quantum identification model for pollutants in the water area to be detected, and inputting the quantum signal response data into the quantum identification model to obtain an initial predicted value of the pollutant concentration in the water area to be detected includes: The quantum fingerprint features of known pollutants in the water body to be detected are analyzed to obtain the adaptive coefficients for pollutant identification and to build a quantum identification model for pollutants. The quantum signal response data is input into the pollutant quantum identification model, and the theoretical estimate of pollutant concentration is obtained by solving the quantum superposition equation. The initial predicted value of pollutant concentration in the water body to be tested is obtained by combining the theoretical estimate of pollutant concentration with the preset detection sensitivity coefficient.

[0007] Preferably, the initial detection parameters for the quantum detection instrument generated based on the initial predicted value of the pollutant concentration include: An initial detection accuracy expectation value is generated based on the quantitative relationship between the background interference intensity of the water area to be detected and the initial predicted value of the pollutant concentration. The initial excitation power parameter and initial sampling frequency parameter of the quantum detection instrument are then generated based on the initial detection accuracy expectation value.

[0008] Preferably, the step of generating the initial excitation power parameters and initial sampling frequency parameters of the quantum detection instrument based on the expected initial detection accuracy includes: A quantum parameter configuration network is established, and the expected value of the initial detection accuracy is used as an input variable to calculate and obtain the initial excitation power parameter and the initial sampling frequency parameter of the quantum detection instrument.

[0009] Preferably, the process of determining the detection error based on the initial deviation data between the actual detection value and the expected detection value, and correcting the initial detection parameters based on the detection error to generate optimized detection parameters for the quantum detection instrument includes: The actual pollutant concentration data of the water area to be tested is collected after the quantum detection instrument performs the initial pollution detection action, and compared with the initial predicted value of the pollutant concentration to determine the detection error; The detection error is analyzed using a quantum parameter adaptive algorithm to generate the excitation power correction and sampling frequency correction for the quantum detection instrument. Based on the excitation power correction amount and the sampling frequency correction amount, the initial excitation power parameters and the initial sampling frequency parameters are corrected to generate optimized excitation power parameters and optimized sampling frequency parameters; Based on the quantum parameter configuration network, the optimized excitation power parameter and the optimized sampling frequency parameter are processed to generate the optimized detection parameters of the quantum detection instrument.

[0010] Preferably, the quantum detection instrument performs secondary pollution detection based on the optimized detection parameters, including: After the secondary pollution detection action is completed, pollutant concentration distribution data of the water area to be tested are collected, and the detection efficiency of the secondary pollution detection action is evaluated.

[0011] Preferably, the method further includes: After obtaining the initial predicted value of pollutant concentration in the water body to be tested, the initial predicted value of pollutant concentration is cross-validated by fusing multispectral quantum response data.

[0012] Preferably, the method further includes: The sampling interval and signal acquisition sensitivity of the quantum sensor array are dynamically adjusted based on the initial deviation data.

[0013] Preferably, the method further includes: Before performing secondary pollution detection, the quantum state excitation threshold in the optimized detection parameters is adjusted in real time based on the rate of change of water temperature.

[0014] The beneficial effects of this invention are as follows: This rapid water pollution detection method driven by quantum sensing technology collects quantum signal response data of the water body to be tested by deploying a quantum sensor array. Relying on the high sensitivity of quantum sensing technology itself, it can capture quantum signal changes caused by low concentrations or even trace amounts of pollutants in the water. Compared with traditional laboratory testing methods, it eliminates the need for complex pretreatment of water samples, and signal acquisition is completed directly in the water body to be tested, effectively avoiding the problem of component changes in water samples during collection and transportation, and ensuring the authenticity of the detection data from the source. At the same time, the quantum sensor array can realize synchronous signal acquisition at multiple points in the water body, significantly shortening the data acquisition time, breaking through the limitations of the cumbersome and time-consuming traditional laboratory testing process, enabling rapid response to the monitoring needs of sudden water pollution events, and providing an efficient technical means for large-scale dynamic monitoring of water bodies. A quantum identification model for pollutants is constructed, and quantum signal response data is input into the model to obtain initial predicted values ​​of pollutant concentrations. Leveraging the model's ability to analyze complex quantum signals, different types of pollutants in water bodies can be accurately identified. Even in complex water environments with multiple pollutants coexisting, the model can effectively distinguish the signal characteristics of each pollutant, reducing interference from other coexisting substances and improving the specificity of identifying complex pollutants. Compared to conventional on-site detection equipment that relies on fixed detection parameters, this method generates initial detection parameters based on initial predicted values, making parameter settings more targeted and avoiding detection errors caused by mismatches between detection parameters and actual water conditions. Based on this, the deviation data between the actual and expected detection values ​​is obtained through initial detection. The detection error is determined based on the deviation data, and the initial detection parameters are corrected. After generating optimized detection parameters, secondary pollution detection is performed, forming a dynamic adjustment mechanism of "prediction-detection-correction-re-detection." This mechanism can optimize detection parameters in real time according to the specific water quality characteristics (such as pH value and turbidity) of the water body being tested, effectively adapting to the detection needs of water bodies with different pollution levels and pollutant types, significantly improving the accuracy and applicability of the detection results. The entire detection process does not rely on large analytical instruments or specialized laboratory environments. The quantum sensor array and related detection equipment can be miniaturized and deployed portablely, reducing the requirements for testing sites and facilitating detection work in complex environments such as remote water areas and emergency sites. Simultaneously, dynamically adjusted detection parameters reduce duplicate testing caused by inappropriate testing plans, lowering consumable consumption and labor costs, and improving the economic efficiency of the detection work. In practical applications, this method can quickly and accurately obtain pollutant concentration information, providing reliable evidence for water pollution control departments to promptly grasp the scope and degree of pollution, helping to quickly formulate scientific and reasonable treatment plans. It can also provide continuous and stable detection support for scenarios such as drinking water safety monitoring and industrial wastewater discharge supervision, promoting the development of water pollution monitoring towards a more efficient, accurate, and economical direction. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the working principle of the rapid water pollution detection method driven by quantum sensing technology described in this invention. Figure 2 This is a schematic diagram illustrating the working principle of the quantum signal response data acquisition process. Figure 3 A schematic diagram illustrating the working principle of the process for obtaining initial predicted values ​​of pollutant concentrations.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] like Figure 1As shown, this application provides a rapid water pollution detection method driven by quantum sensing technology. The method includes deploying a quantum sensor array in the water area to be tested. This array consists of multiple quantum excitation probes used to collect quantum signal response data from the water area. The quantum signal response data includes quantum state transition sequences and spectral interference information, which are acquired through high-frequency sampling and transmitted to a central processing unit. A quantum identification model for pollutants in the water area to be tested is constructed. This model is trained based on the quantum fingerprint characteristics of known pollutants and adaptive coefficients. The collected quantum signal response data is input into the model, and the initial predicted value of pollutant concentration is output by solving the quantum superposition equation. Based on the initial predicted value of pollutant concentration, initial detection parameters for the quantum detection instrument are generated, including excitation power and sampling frequency. The quantum detection instrument uses these parameters to perform an initial pollution detection action, collecting the first deviation data between the actual detection value and the expected detection value. The first deviation data is analyzed to determine the detection error, and a quantum parameter adaptive algorithm is used to correct the initial detection parameters, generating optimized detection parameters. The quantum detection instrument performs a secondary pollution detection action based on the optimized detection parameters, completing the entire detection process.

[0019] In one embodiment, Example 1: See Figure 2 Multiple quantum excitation probes are deployed in the water area to be monitored. These probes are encapsulated in high-strength, corrosion-resistant materials and integrate nitrogen-vacancy color center quantum sensing units, enabling them to exist stably in complex aquatic environments. The spatial distribution of the probes follows a grid design principle, forming a coverage array with a basic interval of 10 meters. One probe unit is deployed at each intersection and connected to the surface control platform via underwater cables. This layout effectively captures quantum signal changes across the cross-sections of the water area. Within a fixed monitoring period, the system acquires quantum state transition response sequences with millisecond-level time resolution. Each probe synchronously emits a microwave excitation signal of a specific frequency and receives spin state evolution data. The sequence data includes parameters such as energy level transition amplitude, phase shift, and relaxation time. The raw data is converted from analog to digital and uploaded to the central processing unit for buffering in real time. Analyzing the water flow characteristics of the monitored water body requires integrating multi-source monitoring data. The velocity vector distribution of the water layer is obtained using an acoustic Doppler current profiler, and combined with the surface flow field model retrieved from satellite remote sensing, the mainstream turbulence intensity and diffusion coefficient are calculated. Information on the distribution of potential pollution sources is obtained from historical environmental monitoring databases, including coordinates of industrial emission points, the extent of agricultural non-point source pollution, and the location of urban drainage outlets. A probability distribution map is generated using spatial interpolation via a geographic information system. The spectral interference weights of different pollution sources are determined through a combination of laboratory calibration and field measurements. For typical pollutant categories such as heavy metals, organic matter, and microorganisms, the coupling degree between their characteristic absorption spectra and quantum signal frequency bands is measured. Principal component analysis is used for dimensionality reduction, and finally, a weight coefficient is assigned to each pollution source category. The weight values ​​range from 0 to 1 and are dynamically updated.

[0020] Constructing a topological model for quantum signal propagation requires establishing a physical network of connections between sensor nodes. Each probe is considered a network node, and the edges connecting nodes represent possible propagation paths for the quantum signal. The weights of the edges are determined by water transmittance, distance attenuation coefficient, and background noise level. The model is mathematically expressed using an adjacency matrix structure from graph theory, and path selection is optimized through an iterative algorithm. After removing high-frequency noise through wavelet transform, the quantum state transition response sequence is convolved with the spectral interference weight matrix to generate a spatiotemporal joint distribution probability cloud map. This map reflects the signal attenuation characteristics and interference superposition effects in the water. When extracting quantum signal response data at time points, the continuous data stream is divided into units of one minute. Key indicators such as peak intensity, average phase angle, and signal-to-noise ratio are extracted within each unit to form a standardized time series dataset for subsequent modeling and analysis. The deployment scheme of quantum-excited probes needs to consider the undulating topography of the water area. A multibeam echo sounder is used to create a three-dimensional digital elevation model of the riverbed or lake bottom. The probe deployment depth is adjusted according to the contour line distribution, ensuring that the sensor array can capture surface signals without missing the diffusion of pollution from the lower layers. Synchronization between probes is controlled by a high-precision atomic clock, with time synchronization errors controlled at the nanosecond level, ensuring the spatiotemporal consistency of the collected data. The fixed monitoring cycle is set based on hydrological variation patterns. During the flood season, a high-frequency acquisition mode (10 samples per second) is used, while during the dry season, a low-frequency mode (1 sample per second) is adopted. This adaptive adjustment mechanism balances data density and energy consumption.

[0021] The analysis of quantum state transition response sequences requires the support of quantum mechanics theory. The raw signals collected by each probe are filtered using a Hamming window and then subjected to a Fast Fourier Transform (FFT) to obtain frequency domain features. The pollutant types corresponding to characteristic peaks are identified from the energy level spectrum. The sequence data also includes environmental parameter correction information. Auxiliary sensors such as water temperature, pH, and conductivity measure basic water parameters in real time, which are used to compensate for the environmental drift effect of the quantum signal. The calculation of spectral interference weights incorporates a fuzzy logic algorithm, using pollutant source type, concentration range, and spectral overlap as input variables. Uncertainty is quantified through a membership function, and the output weight value serves as a correction factor for the topology model. The construction process of the quantum signal propagation topology model includes a dynamic optimization phase. The initial model structure is derived based on ideal fluid dynamics equations, and then corrected using measured flow velocity data and pollutant diffusion coefficients. The Monte Carlo method is used to simulate the probability distribution of the signal propagation path. The model output layer generates a three-dimensional visualization matrix, where the matrix element values ​​represent the signal strength confidence level of each spatial point at a specific time point. The time-node data extraction adopts a sliding window algorithm, and the window length is automatically adjusted according to the signal stability. The extracted data packets are accompanied by timestamps and spatial coordinate information, forming a structured database for the pollutant identification model to call.

[0022] In one embodiment, Example 2: See Figure 3 To analyze the quantum fingerprint characteristics of known pollutants in the water body to be tested, a standard pollutant spectral database needs to be established. This database is constructed through calibration experiments under controlled laboratory conditions: pure pollutants are dissolved in deionized water to form standard solutions with different concentration gradients. A quantum excitation probe is used to scan and obtain characteristic energy level transition spectra, which include parameters such as Raman scattering cross section, fluorescence lifetime, and spin relaxation time. The quantum fingerprint of each pollutant is represented by a 32-dimensional feature vector, whose elements include physical quantities such as resonance frequency shift, Stark effect coefficient, and Zeeman splitting mode. Obtaining the adaptive coefficients for pollutant identification requires a pattern recognition algorithm. The quantum fingerprint features are input into a support vector machine for classification training, and the output is the discriminative weights for different pollutant categories. These weights constitute an adaptive coefficient matrix used to dynamically adjust the model's recognition threshold. The pollutant quantum identification model adopts a deep neural network architecture. The network input layer is designed with 256 neurons corresponding to the feature dimensions of the quantum signal response data. The hidden layer contains 5 fully connected layers and 3 dropout layers to prevent overfitting. The output layer uses a softmax function to generate the pollutant category probability distribution. Model training uses a backpropagation algorithm to optimize the weight parameters, and the loss function is a combination of cross-entropy and mean squared error. The solution of the quantum superposition equation is embedded in the forward propagation process of the neural network. The quantum signal response data is mapped to state vectors in Hilbert space. The quantum state correlation strength of different pollutant components is obtained by calculating the partial trace operation of the density matrix, and finally the theoretical estimate of pollutant concentration is output.

[0023] Before inputting quantum signal response data into the pollutant quantum identification model, data preprocessing is required. The original time-series data undergoes sliding window normalization to eliminate baseline drift. Principal component analysis is used to reduce the data dimensionality to 256 dimensions to match the network input requirements. Missing values ​​are filled using spatiotemporal kriging interpolation. During model inference, approximate solutions to the quantum state superposition equation are calculated in real time. To address the decoherence effect in the aquatic environment, a dephase error compensation algorithm is introduced to correct the theoretical estimates. This algorithm constructs a non-unitary evolution operator based on the Lindbrard master equation. When combining the theoretical pollutant concentration estimate with the preset detection sensitivity coefficient to obtain the initial prediction value, the sensitivity coefficient is dynamically adjusted according to the signal-to-noise ratio of the sensor array: first, the average signal-to-noise ratio of the quantum signal within the current acquisition period is calculated, and a basic sensitivity value is obtained through lookup table mapping; then, linear compensation is performed based on environmental parameters such as water turbidity and temperature change rate to generate the final sensitivity coefficient. The concentration fusion algorithm uses a weighted average method, performing a Hadamard product operation on the theoretical estimate and the sensitivity coefficient matrix to output a three-dimensional distribution map of the initial predicted pollutant concentration.

[0024] The quantum fingerprint feature analysis process incorporates a dynamic update mechanism. When an unknown spectral feature is detected, a calibration procedure is automatically triggered: the system collects suspected contaminated water samples for gas chromatography-mass spectrometry analysis, and the identification results are fed back to the quantum fingerprint database to expand the feature library capacity. The calculation of the pollutant identification adaptive coefficient introduces an incremental learning algorithm, periodically using newly collected field data to fine-tune the classifier parameters, maintaining the model's adaptability to environmental changes. The training dataset for the pollutant quantum identification model includes multi-scenario simulation samples. Pollutant diffusion scenarios under different hydrological conditions are generated using computational fluid dynamics software, and corresponding quantum signal response data are generated by combining quantum mechanics simulations. The total dataset exceeds 100,000 samples. Model validation employs a k-fold cross-validation method, dividing the dataset into training, validation, and test sets to ensure the model's generalization ability meets practical requirements.

[0025] The solution optimization of the quantum superposition equation employs a variational quantum algorithm to simplify computational complexity, discretizing the continuous energy levels into 128 energy level intervals and using variational principles to find the optimal approximate solution for the energy eigenstates. The output format of the theoretical concentration estimate is a tensor structure, containing the concentration value at spatial coordinates, confidence interval, and timestamp information. This data is transmitted to a cloud storage system via an IoT protocol. The preset detection sensitivity coefficient takes into account instrument performance degradation; sensor calibration is performed monthly using standard samples, and sensitivity drift curves are recorded and compensation parameters are updated. The initial prediction generation process includes an outlier filtering mechanism, employing an isolated forest algorithm to identify and remove predictions that significantly deviate from physical laws, ensuring the reliability of the output data.

[0026] Taking a water quality monitoring project in a downstream river section of a chemical plant as an example, the quantum sensor array detected an abnormal quantum signal response, triggering the system to initiate a pollutant quantum identification process. This river section historically exhibits characteristics of combined benzene and heavy metal pollution. The quantum excitation probe collected characteristic fluorescence signals in the 248nm to 380nm wavelength band, along with specific energy level transition spectra corresponding to chromium ions. Laboratory calibration databases showed that the quantum fingerprint of benzene includes a fluorescence peak at 312nm and a fluorescence lifetime of 1.8ns, while hexavalent chromium ions exhibit a significant absorption peak and spin relaxation time at 358nm. The pollutant identification adaptive coefficients were calculated using a machine learning algorithm. The discrimination weight for benzene was set to 0.76, and the weight for heavy metals was 0.68. These coefficients constitute the adjustment parameters of the identification model. The pollutant quantum identification model employs a convolutional neural network architecture. The network input layer is designed with 128 nodes corresponding to the frequency domain feature values ​​of the quantum signal. The hidden layer contains three convolutional layers and two pooling layers for feature extraction. The output layer uses a sigmoid function to generate the probability of pollutant presence. The model was trained using a historical monitoring dataset containing quantum response data from 2000 benzene series samples and 1500 heavy metal samples. An adaptive momentum optimization algorithm was used to adjust the network weights during training. The solution to the quantum superposition equation was embedded in the network's feature extraction layer, mapping the input quantum signal to a state vector in a high-dimensional Hilbert space. The quantum correlation strength of each pollutant was obtained by calculating the reduced density matrix.

[0027] Before inputting quantum signal response data, standardization processing is performed. The raw spectral data is smoothed by Savitzky-Golay filtering, and the data is compressed to the [0,1] interval using the min-max normalization method. Missing values ​​are filled in using time series prediction methods. During model inference, the numerical solution of the quantum state superposition equation is solved in real time. To address the temperature drift effect in water, a temperature compensation algorithm is introduced to correct the calculation results. This algorithm is constructed based on experimental data of quantum state evolution at different temperatures. When combining theoretical estimates of pollutant concentrations with preset detection sensitivity coefficients, the sensitivity coefficients are dynamically adjusted according to the real-time signal-to-noise ratio: first, the signal-to-noise ratio of the current quantum signal in the characteristic band is calculated, and the basic sensitivity value is obtained through a preset mapping curve; then, nonlinear compensation is performed based on the water pH and dissolved oxygen content to generate the final sensitivity coefficients. The concentration fusion process uses a weighted decision algorithm, which performs a dot product operation between the theoretical estimates and the sensitivity coefficient matrix, outputting a list of initial predicted concentrations of benzene compounds and heavy metals.

[0028] The quantum fingerprint feature analysis includes a real-time update function. When an unknown fluorescence spectrum is detected, a calibration procedure is initiated: the system automatically collects water samples for gas chromatography-mass spectrometry analysis, and stores the identification results and quantum signal features together in the database. The pollutant identification adaptive coefficient is updated using an online learning mechanism, fine-tuning the classifier parameters weekly using newly collected field data to maintain the model's ability to identify new pollutants. The training set for the pollutant quantum identification model includes multi-season monitoring data. Pollutant diffusion scenarios under different flow conditions are generated using hydrological simulation software, and corresponding response data are generated by combining quantum simulation, resulting in a total dataset of 5000 samples. Model validation uses a hold-out validation method, using 20% ​​of the data as the test set to evaluate the model's identification accuracy under different pollution levels. The solution to the quantum superposition equation uses a numerical approximation method, discretizing the continuous energy level into 64 energy level intervals, and calculating approximate solutions for energy eigenvalues ​​using a variational method. The output of theoretical concentration estimates includes spatial distribution information; the concentration value at each monitoring point is accompanied by a timestamp and coordinate markers. This data is transmitted to the data center via an IoT protocol. The preset detection sensitivity coefficient takes into account instrument aging factors. The sensor is calibrated monthly using standard samples, and the sensitivity decay curve is recorded and compensation parameters are updated. The generation of initial predicted values ​​includes a data verification process, using box plots to identify and remove outlier predicted values ​​to ensure the reasonableness of the output data.

[0029] In one embodiment, Example 3: An initial expected detection accuracy value is generated based on the quantitative relationship between the background interference intensity and the initial predicted pollutant concentration of the water area to be detected. The calculation of the background interference intensity requires consideration of multiple environmental factors: suspended solids concentration data are obtained using a turbidity sensor attached to the quantum sensor array; thermal fluctuation range is recorded using a temperature sensor; and ion strength parameters are measured using a conductivity sensor. These parameters are normalized and input into the interference assessment model. This model employs a multidimensional linear regression algorithm to output a dimensionless exponent of the background interference intensity, whose value typically fluctuates between 0 and 1. The initial predicted pollutant concentration value comes from the output of the aforementioned quantum recognition model, containing a spatially distributed three-dimensional concentration matrix. The concentration value at each grid point needs to be correlated with the corresponding background interference index. The correlation function takes the following form:

[0030] in: An intermediate variable representing the expected value of the initial detection accuracy. The spectral interference weight represents the i-th sampling point. The predicted pollutant concentration at this location, This represents the variance of temperature fluctuations. The variance of turbidity variation is represented by a logarithmic function used to smooth the nonlinear effects of high concentration values. This calculation process is repeated at each time step, generating a dynamically updated sequence of expected accuracy values.

[0031] The quantum parameter configuration network is constructed using a deep reinforcement learning framework. The network input layer receives the statistical features (including mean, variance, and trend coefficient) of the precision expectation value sequence, the hidden layer contains long short-term memory units to handle time-series dependencies, and the output layer is mapped to the parameter space through a fully connected neural network. Network training uses a historical operation dataset, which records the correspondence between precision expectations and optimal parameters in past detection tasks, and optimizes the network weights using a policy gradient algorithm. The initial excitation power parameter is calculated based on the principle of energy conservation, converting the precision expectation value into an electromagnetic field strength requirement, and then converting it into a power value based on the probe characteristic curve, typically in the milliwatt range. The initial sampling frequency parameter needs to consider signal attenuation characteristics; higher precision expectations require denser sampling intervals to capture rapidly changing quantum state information, and the frequency range is typically adjusted between kilohertz and megahertz. The real-time inference process of the quantum parameter configuration network incorporates multiple verification mechanisms. When a new expected accuracy value is received, the network first checks the validity of the input, eliminating outliers that clearly exceed the physical range. Then, it calculates preliminary parameter suggestions through forward propagation. These suggestions need to be reviewed by the physical constraint module to ensure they do not exceed the instrument's hardware limits. The generation of excitation power parameters also considers energy efficiency optimization, employing an adaptive particle swarm optimization algorithm to find the lowest power setting that meets the accuracy requirements, extending the field operating time of the sensor array. The determination of the sampling frequency parameter simultaneously considers data storage and transmission limitations, optimizing the sampling strategy through compressed sensing theory to minimize the amount of data while ensuring information integrity.

[0032] The modeling of the quantitative relationship between background interference intensity and pollutant concentration employs a dynamic update mechanism. The system periodically collects new environmental monitoring data and recalibrates the regression model coefficients to maintain the accuracy of interference assessment. The calculation of the expected accuracy value is embedded in a real-time quality control loop. Each calculation result is compared with recent historical values ​​for consistency, and a manual review process is triggered when a significant deviation occurs. The training data for the quantum parameter configuration network is continuously expanded. The operational parameters and actual effects of each successful detection task are recorded and added to the training set, enabling the network's parameter recommendation capability to continuously improve over time. The specific assignment process for the initial excitation power parameter includes safety protection logic. The system queries the probe model database to obtain the maximum allowable power limit, ensuring that the recommended parameters will not damage the sensing equipment. The setting of the sampling frequency parameter takes into account the real-time processing capability of the signal processing system. When the recommended frequency exceeds the processor's load limit, the downsampling algorithm is automatically activated, and the expected accuracy value is adjusted synchronously. All parameter generation logs record detailed calculation processes and decision-making basis. These log files are encrypted and stored on a central server for subsequent algorithm auditing and optimization. The process forms an automated pipeline from environmental perception to parameter generation. The background interference intensity is calculated and updated every five minutes, the recalculation cycle for the expected accuracy is one minute, and the inference process of the parameter configuration network is completed within seconds. This design ensures that the detection system can respond quickly to environmental changes and adjust instrument settings in a timely manner to achieve the best detection results. The parameter generation module and the hardware control unit interact using a standardized communication protocol. The generated parameter set is sent to each quantum detection probe via digital commands, and the microcontroller inside the probe adjusts its working state according to the commands.

[0033] In one embodiment, Example 4: After the quantum detection instrument performs the initial pollution detection, the system initiates the actual pollutant concentration data acquisition process. Taking a monitoring point in a river basin as an example, the deployed quantum sensor array covers a 200-meter river section and includes 12 probe nodes. The initial detection uses initial parameter settings: excitation power of 85 milliwatts and sampling frequency of 120 kilohertz. After the detection is completed, the water samples collected by each probe node undergo laboratory standard analysis using a portable mass spectrometer to obtain actual pollutant concentration data, while simultaneously recording the latitude and longitude coordinates and water depth information of the collection point. These measured data are sent to the central processing unit via a wireless transmission module for spatial matching and temporal alignment with the previously generated initial predicted pollutant concentration values. The comparison process employs a multi-dimensional error analysis algorithm, dividing the monitoring area into 50×50-meter grid cells, and calculating the absolute and relative deviations between the predicted and measured values ​​within each cell. The system pays particular attention to the comparison results of downstream areas of key pollution sources, as these areas typically exhibit more complex diffusion patterns. The determination of detection error takes into account both spatial distribution characteristics and temporal fluctuations. It not only calculates the overall average error but also statistically analyzes the dispersion and spatial autocorrelation of the error, ultimately generating an evaluation report that includes the error value and confidence interval.

[0034] The quantum parameter adaptive algorithm operates based on the principle of error gradient analysis. During algorithm initialization, the instrument performance parameter database is loaded, including the power adjustment range and frequency response characteristics of different probe models. The core of the algorithm employs a perturbation observation method, observing error trends through minor adjustments to parameter values ​​to calculate the correction direction and magnitude of power and frequency. The calculation of the excitation power correction considers energy transfer efficiency, determining the adjustment step size based on the error magnitude and sign. Negative errors (measured values ​​lower than predicted values) typically require increased excitation intensity, while positive errors require a corresponding reduction in power output. The determination of the sampling frequency correction involves simultaneous analysis of the signal attenuation curve; higher error regions often require increased sampling density to capture more detailed quantum state evolution information. The application of the correction is achieved through a parameter mapping function, with the original parameters and correction value as input and the optimized parameter values ​​as output. Power correction uses a linear superposition method, while frequency correction considers a logarithmic scale to accommodate magnitude changes. The calculation results of optimized excitation power parameters require a safety verification module to ensure they do not exceed the probe's maximum power capacity. Optimization of sampling frequency parameters requires verification of compatibility with the data acquisition system to avoid exceeding the processor cache capacity. After parameter optimization, a new detection instruction set is generated, which includes updated power values, frequency values, and corresponding timing control information.

[0035] The reprocessing of the quantum parameter configuration network incorporates a multi-objective optimization strategy. The network receives optimized power and frequency parameters as input and evaluates the comprehensive performance of parameter combinations in terms of energy consumption, accuracy, and stability. A game-theoretic decision-making algorithm in the network's hidden layer calculates the Pareto front for different parameter combinations and selects the optimal equilibrium point. The final output format of the optimized detection parameters includes binary control instructions and a human-readable configuration file. The control instructions are sent to the probe controller via the industrial Ethernet protocol, and the configuration file is archived for auditing and traceability. The execution of secondary pollution detection adopts a phased initiation strategy. Pilot detection is first conducted in the three grid areas with the largest errors to verify the effectiveness of the parameters, and then gradually expanded to the entire area. During the detection process, signal quality indicators are monitored in real time, and an adaptive degradation mechanism is triggered when abnormal noise occurs. After detection, the collected pollutant concentration distribution data is used to generate a spatial contour map using Kriging interpolation, marking hotspot areas and the direction of concentration gradient changes.

[0036] The detection efficiency evaluation employs a multi-index comprehensive analysis method, primarily considering the improvement in data quality before and after parameter adjustment, the change in total detection task time, and system resource consumption. The evaluation results are presented in a structured report, including quantitative indicators and qualitative analysis opinions. The entire calibration and optimization process forms a closed-loop feedback circuit. The error data from this detection is automatically added to the training database to improve the accuracy of subsequent parameter recommendation algorithms (see Table 1).

[0037] Table 1: Sample Data for Initial Detection Error Analysis

[0038] During error analysis, a systematic bias pattern was discovered in specific regions. Grid points located at river bends generally exhibited positive error characteristics (measured values ​​higher than predicted values), while straight river sections showed a negative error trend. This spatial distribution characteristic was incorporated into the correction calculation algorithm, employing differentiated correction strategies for different terrain regions. The allocation of power correction considered the probe spacing factor, granting a larger adjustment margin to nodes located at the array edge, while using a relatively conservative correction amplitude for the central nodes. The frequency correction setting was correlated with water flow velocity; higher flow velocity areas correspondingly increased the sampling frequency to compensate for the time delay effect during signal transmission. The validation of optimized detection parameters employed a cross-validation method, dividing the monitoring area into training and test sets to ensure the applicability of parameters under different geographical conditions. Data acquisition for secondary detection actions included recording auxiliary environmental parameters, including real-time flow velocity, flow direction, and temperature data, which were used for subsequent analysis of the impact of environmental factors on detection accuracy. The final output pollutant concentration distribution map included an uncertainty assessment layer, using color intensity to represent the measurement confidence level of each point.

[0039] In one embodiment, Example 5: After obtaining the initial predicted value of pollutant concentration in the water area to be detected, the system initiates the fusion verification process of multispectral quantum response data. Taking the monitoring of a downstream river section of a chemical industrial park as an example, the quantum sensor array simultaneously collects quantum response data in the ultraviolet (250-400nm), visible (400-700nm), and near-infrared (700-1100nm) bands. After preprocessing, the data of each band are input into the corresponding spectral analysis module. The ultraviolet band focuses on detecting the fluorescence signal generated by aromatic organic compounds, the visible band analyzes the scattering effect caused by turbidity, and the near-infrared band captures the vibrational absorption characteristics of water molecules. Cross-validation adopts a decision-level fusion strategy, inputting the concentration estimates obtained from the three bands into the DS evidence theory model to calculate the consistency and conflict degree of the predicted values ​​at each point. When the conflict degree exceeds the threshold, a re-detection mechanism is triggered. During the fusion process, a spectral reliability weight matrix is ​​established. The weight values ​​are dynamically adjusted based on the signal-to-noise ratio and historical accuracy of each band. The weight coefficient for the ultraviolet band is typically set in the range of 0.35-0.45, for the visible band it is 0.25-0.35, and for the near-infrared band it is 0.20-0.30. The validation results generate a spatial consistency distribution map, which marks the degree of matching between the multispectral validation results and the initial predicted values ​​for each grid cell. Regions with a matching degree below 85% are automatically marked as areas requiring further verification.

[0040] Based on the initial deviation data, the sampling interval and signal acquisition sensitivity of the quantum sensor array are dynamically adjusted. The system analyzes the deviation distribution pattern found in the initial detection: for areas with an absolute deviation exceeding 15 μg / L, the sampling interval is shortened from the default 5 minutes to 2 minutes; for areas with deviations between 5 and 15 μg / L, the sampling interval is adjusted to 3 minutes; and for areas with deviations below 5 μg / L, the original sampling interval is maintained. The signal acquisition sensitivity adjustment adopts a tiered strategy: the probe gain is increased by 3 dB in high-deviation areas, by 1.5 dB in medium-deviation areas, and remains unchanged in low-deviation areas. The adjustment algorithm considers probe power consumption constraints; when the battery level is below 50%, an energy-saving mode is automatically activated, and the adjustment range is reduced proportionally. The dynamic adjustment of the sampling interval introduces a sliding window mechanism; the system calculates the moving average deviation over the most recent 10 sampling periods in real time, and triggers a warning-style interval compression when the average value continues to rise. The signal acquisition sensitivity adjustment also considers ambient light conditions; at night, the gain is automatically increased by 0.5 dB to compensate for ambient noise, and under strong light conditions, the gain is correspondingly reduced to prevent signal saturation. All adjustment parameters are sent to the probe node via encrypted data packets. After the node controller confirms that the parameters are effective, it returns an execution status code.

[0041] Before performing secondary pollution detection, the system initiates real-time monitoring and parameter correction of the water temperature change rate: a high-precision temperature sensor (measurement accuracy ±0.1℃) is integrated into each probe node to collect water temperature data at 30-second intervals, calculating the absolute value of the temperature change rate per minute. The quantum state excitation threshold is corrected using a temperature compensation algorithm, establishing a threshold adjustment function with temperature as the independent variable. For every 1℃ increase in temperature, the threshold is decreased by 0.5mV, and for every 1℃ decrease, the threshold is increased by 0.5mV. An inertial delay mechanism is introduced in the correction process: when the temperature change rate exceeds 0.5℃ / minute, the correction is delayed by 60 seconds to avoid over-adjustment. The threshold correction parameters are generated through distributed computing. Each probe node independently calculates its local correction value and uploads it to the central node for consistency verification. When the difference in correction values ​​between adjacent nodes exceeds 15%, a collaborative calibration process is triggered. The final determination of optimized detection parameters uses a voting mechanism; a correction scheme agreed upon by more than two-thirds of the nodes is adopted and implemented. The corrected parameter set includes updated excitation thresholds, power parameters, and frequency parameters, which are packaged into a detection instruction set and distributed to all probes.

[0042] The processing of multispectral validation data includes outlier identification. When data in a certain band deviates significantly from other bands, the system automatically checks the calibration status of the sensor in that band and triggers an online calibration procedure if necessary. All parameter changes during the dynamic adjustment process are recorded in the operation log, including adjustment time, adjustment magnitude, adjustment reason, and execution effect. These logs are used for subsequent optimization of the adjustment algorithm. The temperature correction procedure includes a fault-safe mechanism that automatically switches to the last known normal value when the temperature sensor reading fluctuates abnormally, preventing erroneous corrections from causing detection failure. Multispectral data validation ensures data reliability from a spatial dimension, the dynamic adjustment mechanism improves monitoring adaptability from a temporal dimension, and real-time temperature correction enhances system robustness from an environmental dimension. All operations are executed automatically in the background without manual intervention, and alarm information is sent to the monitoring platform only when a major anomaly is detected.

[0043] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A rapid detection method for water pollution driven by quantum sensing technology, characterized in that, The method includes: A quantum sensor array is deployed in the water area to be tested, and quantum signal response data in the water area is collected; A quantum identification model for pollutants in the water area to be detected is constructed, and the quantum signal response data is input into the quantum identification model to obtain an initial predicted value of pollutant concentration in the water area to be detected. Initial detection parameters for the quantum detection instrument are generated based on the initial predicted values ​​of the pollutant concentration. After the quantum detection instrument performs the initial pollution detection action based on the initial detection parameters, it collects the first deviation data between the actual detection value and the expected detection value. The detection error is determined based on the initial deviation data between the actual detection value and the expected detection value, and the initial detection parameters are corrected based on the detection error to generate optimized detection parameters for the quantum detection instrument. The quantum detection instrument performs secondary pollution detection based on the optimized detection parameters.

2. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The process of deploying a quantum sensor array in the water area to be detected and collecting quantum signal response data in the water area to be detected includes: Multiple sets of quantum excitation probes are deployed in the water area to be tested, and the quantum state transition response sequence of the water area to be tested is collected within a fixed monitoring period; Analyze the water flow characteristics and potential pollution source distribution of the water body to be tested, and obtain the spectral interference weights of different pollution sources; Based on the quantum state transition response sequence and the spectral interference weights of different pollution sources, a quantum signal propagation topology model of the water area to be tested is constructed, and quantum signal response data of the water area to be tested is extracted according to time nodes.

3. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The process of constructing a quantum identification model for pollutants in the water area to be detected, and inputting the quantum signal response data into the model to obtain an initial predicted value of the pollutant concentration in the water area to be detected, includes: The quantum fingerprint features of known pollutants in the water body to be detected are analyzed to obtain the adaptive coefficients for pollutant identification and to build a quantum identification model for pollutants. The quantum signal response data is input into the pollutant quantum identification model, and the theoretical estimate of pollutant concentration is obtained by solving the quantum superposition equation. The initial predicted value of pollutant concentration in the water body to be tested is obtained by combining the theoretical estimate of pollutant concentration with the preset detection sensitivity coefficient.

4. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The initial detection parameters for the quantum detection instrument generated based on the initial predicted value of the pollutant concentration include: An initial detection accuracy expectation value is generated based on the quantitative relationship between the background interference intensity of the water area to be detected and the initial predicted value of the pollutant concentration. The initial excitation power parameter and initial sampling frequency parameter of the quantum detection instrument are then generated based on the initial detection accuracy expectation value.

5. The rapid water pollution detection method driven by quantum sensing technology as described in claim 4, characterized in that, The process of generating the initial excitation power parameters and initial sampling frequency parameters of the quantum detection instrument based on the expected initial detection accuracy includes: A quantum parameter configuration network is established, and the expected value of the initial detection accuracy is used as an input variable to calculate and obtain the initial excitation power parameter and the initial sampling frequency parameter of the quantum detection instrument.

6. The rapid water pollution detection method driven by quantum sensing technology as described in claim 5, characterized in that, The process of determining the detection error based on the initial deviation data between the actual and expected detection values, and correcting the initial detection parameters based on the detection error to generate optimized detection parameters for the quantum detection instrument includes: The actual pollutant concentration data of the water area to be tested is collected after the quantum detection instrument performs the initial pollution detection action, and compared with the initial predicted value of the pollutant concentration to determine the detection error; The detection error is analyzed using a quantum parameter adaptive algorithm to generate the excitation power correction and sampling frequency correction for the quantum detection instrument. Based on the excitation power correction amount and the sampling frequency correction amount, the initial excitation power parameters and the initial sampling frequency parameters are corrected to generate optimized excitation power parameters and optimized sampling frequency parameters; Based on the quantum parameter configuration network, the optimized excitation power parameter and the optimized sampling frequency parameter are processed to generate the optimized detection parameters of the quantum detection instrument.

7. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The quantum detection instrument performs secondary pollution detection based on the optimized detection parameters, including: After the secondary pollution detection action is completed, pollutant concentration distribution data of the water area to be tested are collected, and the detection efficiency of the secondary pollution detection action is evaluated.

8. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The method further includes: After obtaining the initial predicted value of pollutant concentration in the water body to be tested, the initial predicted value of pollutant concentration is cross-validated by fusing multispectral quantum response data.

9. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The method further includes: The sampling interval and signal acquisition sensitivity of the quantum sensor array are dynamically adjusted based on the initial deviation data.

10. The rapid water pollution detection method driven by quantum sensing technology as described in claim 1, characterized in that, The method further includes: Before performing secondary pollution detection, the quantum state excitation threshold in the optimized detection parameters is adjusted in real time based on the rate of change of water temperature.

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