A water quality pollution rapid detection method driven by quantum sensing technology

Abstract

The present application relates to the technical field of water quality pollution detection, and particularly relates to a water quality pollution rapid detection method driven by quantum sensing technology. The present application deploys a quantum sensor array in a water area to be detected to collect quantum signal response data of the water area; a pollutant quantum recognition model is constructed, the quantum signal response data is input into the model to obtain an initial prediction value of pollutant concentration; initial detection parameters of a quantum detector are generated based on the initial prediction value, the detector performs initial pollution detection according to the parameters, and first deviation data of actual detection values and expected detection values is collected; the detection error is determined according to the deviation data, and then the initial detection parameters are corrected to generate optimized detection parameters; finally, the detector performs secondary pollution detection based on the optimized detection parameters. The method can quickly and accurately complete water quality pollution detection without complex water sample pretreatment, is suitable for different water area detection requirements, and improves detection efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality pollution detection, in particular to a quantum sensing technology driven water quality pollution rapid detection method. BACKGROUND

[0002] In the current water environment monitoring system, water quality pollution detection technology is an important means to protect water resources safety, and is widely used in drinking water safety monitoring, industrial wastewater discharge supervision, natural water body ecological protection and other scenes. Traditional water quality pollution detection methods mostly rely on laboratory analysis technology, such as chromatography, mass spectrometry and spectrophotometry. This kind of method usually needs to collect water samples on site first, then transport the water samples to a professional laboratory, after a complex pretreatment process, use large-scale analytical instruments to determine the concentration of pollutants. However, this kind of detection method has obvious limitations. The water sample is easy to be affected by temperature, light, oxygen and other external factors during collection, transportation and storage, which leads to changes in the composition of pollutants, and then affects the authenticity and reliability of the detection results; the laboratory analysis process is complicated, and a single detection usually takes several hours or even several days to complete, which is difficult to meet the requirements of high-speed detection in scenes such as emergency monitoring of sudden water pollution and dynamic monitoring of large-scale water areas.

[0003] With the development of technology, on-site rapid detection technology has gradually emerged, such as portable detection equipment based on electrochemical sensors and optical sensors. Although this kind of equipment can realize on-site detection and shorten the detection time, its detection sensitivity and specificity are still insufficient due to the detection principle of the sensor itself. For low-concentration and complex-component pollutants, conventional sensors are easy to be disturbed by other coexisting substances in water, resulting in large detection error and unable to accurately identify and quantify the pollutants. In addition, most of the existing on-site detection equipment uses fixed detection parameters, which is difficult to dynamically adjust according to the water quality characteristics of different water areas, further reducing the applicability and accuracy of the detection results. In practical application, in the face of water areas with different pollution levels and different types of pollutants, conventional detection methods often need to adjust the detection scheme multiple times and repeatedly detect and verify, which not only increases the detection cost and workload, but also makes it difficult to realize rapid and accurate assessment of water pollution conditions, and cannot provide timely and effective technical support for water pollution control decision-making. SUMMARY

[0004] The main purpose of the present application is to provide a quantum sensing technology driven water quality pollution rapid detection method, which aims to solve the technical problems in the prior art.

[0005] The present application provides a quantum sensing technology driven water quality pollution rapid detection method, which comprises:

[0006] 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;

[0007] constructing a quantum recognition model of pollutants in the water area to be detected, and inputting the quantum signal response data into the quantum recognition model of pollutants to obtain an initial predicted value of pollutant concentration in the water area to be detected;

[0008] generating initial detection parameters of a quantum detection instrument based on the initial predicted value of pollutant concentration;

[0009] collecting first deviation data of actual detection values and expected detection values after the quantum detection instrument performs a first pollution detection action based on the initial detection parameters;

[0010] determining a detection error based on the first deviation data of actual detection values and expected detection values, and correcting the initial detection parameters based on the detection error to generate optimized detection parameters of the quantum detection instrument;

[0011] the quantum detection instrument performs a second pollution detection action based on the optimized detection parameters.

[0012] As preferred, the 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 comprises:

[0013] deploying multiple groups of quantum excitation probes in the water area to be detected, and collecting quantum state transition response sequences of the water area to be detected within a fixed monitoring period;

[0014] analyzing water flow characteristics and potential pollution source distribution of the water area to be detected to obtain spectral interference weights of different pollution sources;

[0015] constructing a quantum signal propagation topology model of the water area to be detected based on the quantum state transition response sequences and the spectral interference weights of different pollution sources, and extracting quantum signal response data of the water area to be detected according to time nodes.

[0016] As preferred, the constructing a quantum recognition model of pollutants in the water area to be detected, and inputting the quantum signal response data into the quantum recognition model of pollutants to obtain an initial predicted value of pollutant concentration in the water area to be detected comprises:

[0017] analyzing quantum fingerprint characteristics of known pollutants in the water area to be detected to obtain pollutant recognition adaptive coefficients, and building a pollutant quantum recognition model;

[0018] inputting the quantum signal response data into the pollutant quantum recognition model to obtain a theoretical estimated value of pollutant concentration by solving quantum state superposition equations;

[0019] The initial predicted value of the pollutant concentration of the water area to be detected is obtained based on the theoretical estimated value of the pollutant concentration and a preset detection sensitivity coefficient.

[0020] Preferably, the initial detection parameters of the quantum detection instrument are generated based on the initial predicted value of the pollutant concentration, including:

[0021] The initial detection accuracy expectation value is generated according to the quantitative relationship between the background interference intensity of the water area to be detected and the initial predicted value of the pollutant concentration, and the initial excitation power parameter and the initial sampling frequency parameter of the quantum detection instrument are generated according to the initial detection accuracy expectation value.

[0022] Preferably, the initial excitation power parameter and the initial sampling frequency parameter of the quantum detection instrument are generated according to the initial detection accuracy expectation value, including:

[0023] The quantum parameter configuration network is established, the initial detection accuracy expectation value is taken as an input variable, and the initial excitation power parameter and the initial sampling frequency parameter of the quantum detection instrument are calculated and obtained.

[0024] Preferably, the detection error is determined based on the first deviation data of the actual detection value and the expected detection value, and the initial detection parameters are corrected based on the detection error to generate the optimized detection parameters of the quantum detection instrument, including:

[0025] The actual pollutant concentration data of the water area to be detected after the quantum detection instrument performs the initial pollution detection action is collected and compared with the initial predicted value of the pollutant concentration to determine the detection error;

[0026] The detection error is analyzed by using the quantum parameter adaptive algorithm to generate the excitation power correction amount and the sampling frequency correction amount of the quantum detection instrument;

[0027] The initial excitation power parameter and the initial sampling frequency parameter are modified based on the excitation power correction amount and the sampling frequency correction amount to generate the optimized excitation power parameter and the optimized sampling frequency parameter;

[0028] The optimized excitation power parameter and the optimized sampling frequency parameter are processed according to the quantum parameter configuration network to generate the optimized detection parameters of the quantum detection instrument.

[0029] Preferably, the quantum detection instrument performs the secondary pollution detection action based on the optimized detection parameters, including:

[0030] After the secondary pollution detection action is completed, the pollutant concentration distribution data of the water area to be detected after the secondary detection is completed is collected, and the detection efficiency of the secondary pollution detection action is evaluated.

[0031] Preferably, the method further comprises:

[0032] After obtaining the initial prediction value of the pollutant concentration of the water area to be detected, the multispectral quantum response data is fused to cross-verify the initial prediction value of the pollutant concentration.

[0033] Preferably, the method further comprises:

[0034] Based on the first bias data, the sampling interval and signal acquisition sensitivity of the quantum sensor array are dynamically adjusted.

[0035] Preferably, the method further comprises:

[0036] Before performing the secondary pollution detection action, the quantum state excitation threshold in the optimized detection parameter is corrected in real time according to the water temperature change rate.

[0037] The water quality pollution rapid detection method driven by the quantum sensing technology can capture the quantum signal change caused by low-concentration or even trace pollutants in the water body by deploying a quantum sensor array to collect quantum signal response data of the water area to be detected, relying on the high sensitivity characteristics of the quantum sensing technology. Compared with the traditional laboratory detection method, the signal acquisition can be directly completed in the water area to be detected without complex pretreatment of the water sample, effectively avoiding the composition change problem of the water sample in the collection and transportation process, and ensuring the authenticity of the detection data from the source. At the same time, the quantum sensor array can realize synchronous signal acquisition of multiple points in the water area, greatly shorten the data acquisition time, break through the limitation of the traditional laboratory detection process, and quickly respond to the monitoring demand of the sudden water pollution event. It also provides an efficient technical means for large-scale water area dynamic monitoring.

[0038] The quantum signal response data is input into the constructed pollutant quantum recognition model to obtain an initial prediction value of the pollutant concentration. With the analysis capability of the model for complex quantum signals, different types of pollutants in the water body can be accurately recognized. Even in the presence of multiple pollutants in the complex water quality environment, the signal characteristics corresponding to each pollutant can be effectively distinguished, the interference of other coexisting substances in the water body on the detection result is reduced, and the recognition specificity of the complex component pollutant is improved. Compared with the conventional on-site detection equipment which relies on fixed detection parameters, the initial detection parameters are generated based on the initial prediction value in the method, so that the setting of the detection parameters is more targeted, and the detection error caused by the mismatch between the detection parameters and the actual situation of the water area is avoided. On this basis, the deviation data of the actual detection value and the expected detection value is obtained through the first detection, the detection error is determined based on the deviation data, the initial detection parameters are corrected, the optimized detection parameters are generated, and the second pollution detection is performed, forming a dynamic adjustment mechanism of "prediction-detection-correction-redetection", which can optimize the detection parameters in real time according to the specific water quality characteristics (such as pH value, turbidity, etc.) of the water area to be detected, effectively adapt to the water area detection requirements of different pollution degrees and different pollutant types, and significantly improve the accuracy and applicability of the detection result.

[0039] The entire detection process does not need to rely on large-scale analytical instruments and professional laboratory environment. The quantum sensor array and related detection equipment can be miniaturized and deployed in a portable manner, which reduces the requirements for the detection site and facilitates the detection work in remote water areas, emergency sites and other complex environments. At the same time, the dynamically adjusted detection parameters reduce repeated detection caused by improper detection schemes, reduce the consumption of consumables and labor cost in the detection process, and improve the economy of the detection work. In practical application, the method can quickly and accurately obtain the pollutant concentration information, provide reliable basis for the water pollution control department to timely grasp the pollution range and pollution degree, help to quickly develop a scientific and reasonable treatment scheme, and can also provide continuous and stable detection support for drinking water safety monitoring, industrial wastewater discharge supervision and other scenes, and promote the development of water quality pollution monitoring work in a more efficient, more accurate and more economical direction. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A working principle diagram of the quantum sensing technology-driven water quality pollution rapid detection method described in the application;

[0041] Figure 2 A working principle diagram of the quantum signal response data acquisition process;

[0042] Figure 3 A working principle diagram of the initial prediction value acquisition process of the pollutant concentration.

[0043] The implementation of the purpose of the application, functional characteristics and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0044] It is to be understood that the specific examples described herein are merely illustrative of the present application and are not intended to limit the scope of the present application.

[0045] As Figure 1 shown, the present application provides a water pollution rapid detection method driven by quantum sensing technology, including deploying a quantum sensor array in the water area to be detected, the array is composed of multiple quantum excitation probes, which are used to collect quantum signal response data in the water area; the quantum signal response data includes quantum state transition sequence and spectral interference information, these data are obtained by high-frequency sampling and transmitted to a central processing unit. Construct a pollutant quantum identification model of the water area to be detected, the model is trained based on quantum fingerprint characteristics of known pollutants and adaptive coefficients; input the collected quantum signal response data into the model, output the initial predicted value of pollutant concentration by solving quantum state superposition equation. Based on the initial predicted value of pollutant concentration, generate initial detection parameters of quantum detection instrument, including excitation power and sampling frequency; the quantum detection instrument uses these parameters to perform the first pollution detection action, collects the first deviation data of actual detection value and expected detection value. Analyze the first deviation data to determine the detection error, correct the initial detection parameters using quantum parameter adaptive algorithm, generate optimized detection parameters; the quantum detection instrument performs the second pollution detection action based on the optimized detection parameters, completes the entire detection process.

[0046] In one embodiment, embodiment 1: refer to Figure 2In the water area to be detected, multiple sets of quantum excitation probes are arranged, which are packaged with high-strength corrosion-resistant materials, and integrate nitrogen vacancy color center quantum sensing units inside, which can stably exist in complex water environments; the spatial distribution of the probes follows the grid design principle, forming a coverage array with a basic interval of 10 meters, with a probe unit deployed at each intersection, connected to the water surface control platform through underwater cables. This layout effectively captures the quantum signal changes in the horizontal and vertical sections of the water area. Within a fixed monitoring period, the system collects quantum state transition response sequences with millisecond-level time resolution, each probe synchronously emits a specific frequency microwave excitation signal and receives spin state evolution data, the sequence data contains parameters such as energy level transition amplitude, phase shift and relaxation time, and the original data is converted to digital and uploaded to the central processing unit for caching in real time. To analyze the water flow characteristics of the water area to be detected, multiple source monitoring data need to be integrated, the acoustic Doppler current profiler is used to obtain the water layer flow velocity vector distribution, combined with the surface flow field pattern retrieved by satellite remote sensing, the main flow direction turbulence intensity and diffusion coefficient are calculated; the potential pollution source distribution information is derived from the environmental monitoring historical database, including industrial emission point coordinates, agricultural non-point source pollution range and urban drainage outlet location, and the spatial interpolation is generated by using geographic information system to generate probability distribution map. The spectral interference weight of different pollution sources is determined by combining laboratory calibration and field measurement, for typical pollution categories such as heavy metals, organic matter and microorganisms, the coupling degree of their characteristic absorption spectrum and quantum signal frequency band is measured, principal component analysis is used for dimensionality reduction processing, and finally each pollution source category is given a weight coefficient, the weight value is between 0 and 1 and is dynamically updated.

[0047] The construction of the quantum signal propagation topology model needs to establish the physical association network between sensor nodes. Each probe is regarded as a network node, and the connection edge between nodes represents the possible propagation path of quantum signals. The weight of the edge is determined by the water transmittance, distance attenuation coefficient and background noise level. The model uses the adjacency matrix structure in graph theory for mathematical expression, and optimizes the path selection through iterative algorithm. After the quantum state transition response sequence is removed by wavelet transform, the convolution operation is performed with the spectral interference weight matrix to generate the probability cloud map of the space-time joint distribution. This map can reflect the signal attenuation characteristics and interference superposition effect in the water body. According to the time node, the quantum signal response data is extracted, and each minute is taken as the basic time unit to segment the continuous data stream. The peak intensity, average phase angle and signal-to-noise ratio are extracted in each unit to form a standardized time series data set for subsequent modeling analysis. The layout scheme of the quantum excitation probe needs to consider the changes of the water terrain. A multi-beam depth sounder is used to draw a three-dimensional digital elevation model of the river bed or lake bottom. According to the contour distribution, the deployment depth of the probe is adjusted to make the sensing array capture the surface signal without missing the bottom pollution diffusion. The synchronous triggering between probes is controlled by a high-precision atomic clock, and the time synchronization error is controlled within nanoseconds to ensure the space-time consistency of the collected data. The setting of the fixed monitoring period refers to the hydrological change law. In the flood season, the high-frequency acquisition mode (10 samples per second) is adopted, and in the dry season, the low-frequency mode (1 sample per second) is adopted. This adaptive adjustment mechanism balances the relationship between data density and energy consumption.

[0048] The analysis of the quantum state transition response sequence needs the support of quantum mechanics theory. After the original signal collected by each probe is filtered by Hamming window, the frequency domain characteristics are obtained by fast Fourier transform, and the characteristic peak position corresponding to the pollutant type is identified from the energy level spectrum. The sequence data also contains environmental parameter correction information. The auxiliary sensors such as water temperature, pH value and conductivity measure the basic parameters of the water body in real time, which are used to compensate the environmental drift effect of quantum signals. The calculation of spectral interference weight introduces fuzzy logic algorithm, taking the type of pollution source, concentration range and spectral overlap degree as input variables, quantifying the uncertainty through membership function, and outputting weight value as correction factor of the topology model. The construction process of the quantum signal propagation topology model includes dynamic optimization. The initial structure of the model is based on the ideal fluid mechanics equation, and then the measured flow rate data and pollutant diffusion coefficient are corrected. The Monte Carlo method is used to simulate the probability distribution of signal propagation path. The output layer of the model generates a three-dimensional visualization matrix, and the matrix element value represents the signal intensity confidence of each spatial point at a specific time point. The sliding window algorithm is used for time node data extraction, and the window length is automatically adjusted according to the signal stability. The extracted data packet contains timestamp and spatial coordinate information, forming a structured database for the pollutant identification model to call.

[0049] In one embodiment, embodiment 2: see Figure 3, the quantum fingerprint characteristics of known pollutants in the water area to be detected need to establish a standard pollutant spectrum database, which is constructed by calibration experiments in a laboratory controlled environment: pure pollutants are dissolved in deionized water to form standard solutions with different concentration gradients, and characteristic energy level transition maps are obtained by scanning with a quantum excitation probe. The maps 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, and the vector elements include physical quantities such as resonance frequency offset, Stark effect coefficient, and Zeeman splitting pattern. To obtain adaptive coefficients for pollutant identification, a pattern recognition algorithm is used to input the quantum fingerprint characteristics into a support vector machine for classification training, and the discrimination weights of different pollutant categories are output. These weights form the adaptive coefficient matrix used to dynamically adjust the model recognition threshold. The quantum recognition model of pollutants is built using a deep neural network architecture. The input layer of the network is designed with 256 neuron nodes corresponding to the feature dimension of the quantum signal response data, the hidden layer contains 5 fully connected layers and 3 dropout layers to prevent overfitting, and the output layer uses a softmax function to generate a probability distribution of pollutant categories. The back propagation algorithm is used for model training to optimize the weight parameters, and the loss function uses a composite form of cross-entropy and mean square error. The solution of the quantum state superposition equation is embedded in the forward propagation process of the neural network, which maps the quantum signal response data to the state vector in Hilbert space. The quantum state correlation strength of different pollutant components is obtained by calculating the trace operation of the density matrix, and finally the theoretical estimated value of the pollutant concentration is output.

[0050] The quantum signal response data needs to be preprocessed before inputting into the pollutant quantum recognition model. The original time series data is normalized by sliding window to eliminate baseline drift, and principal component analysis is used to reduce the data dimension to 256 dimensions to match the network input requirements. The missing values are completed by the spatiotemporal Kriging interpolation method. In the model inference process, the approximate solution of the quantum state superposition equation is calculated in real time. In view of the decoherence effect existing in the water environment, a decoherence error compensation algorithm is introduced to correct the theoretical estimated value, which is based on the Lindblad master equation to construct a non-unitary evolution operator. When the initial predicted value is obtained combining the theoretical estimated value of the pollutant concentration and the preset detection sensitivity coefficient, the sensitivity coefficient is dynamically adjusted according to the signal-to-noise ratio of the sensor array: first, calculate the average signal-to-noise ratio of the quantum signal in the current acquisition period, and obtain the basic sensitivity value by looking up the table; then, linear compensation is performed according to 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 to perform Hadamard product operation on the theoretical estimated value and the sensitivity coefficient matrix, and outputs a three-dimensional distribution map of the initial predicted value of the pollutant concentration.

[0051] The analysis process of quantum fingerprint features contains a dynamic updating mechanism that automatically triggers the calibration procedure when unknown spectral features are detected: the system collects suspected contaminated water samples for gas chromatography-mass spectrometry analysis, and feeds the identification results back to the quantum fingerprint database to expand the feature library capacity. The calculation of the adaptive coefficient of pollutant identification introduces an incremental learning algorithm, which regularly uses newly collected field data to fine-tune the classifier parameters, maintaining the model's adaptability to environmental changes. The training data set of the pollutant quantum recognition model contains multiple scene simulation samples, and the computational fluid dynamics software generates pollutant diffusion scenes under different hydrological conditions. Combined with quantum mechanics simulation, the corresponding quantum signal response data is generated, and the total amount of data set exceeds 100,000 samples. Model verification uses the k-fold cross-validation method to divide the data set into training set, validation set and test set, ensuring that the model's generalization ability meets practical requirements.

[0052] The solution of the quantum state superposition equation is optimized using a variational quantum algorithm to simplify the calculation complexity. The continuous energy level is discretized into 128 energy level intervals, and the optimal approximate solution of the energy eigenstate is found through the variational principle. The output format of the concentration theoretical estimate value is a tensor structure, including the concentration value, confidence interval and time stamp information of the spatial coordinate point. These data are transmitted to the cloud storage system through the Internet of Things protocol. The setting of the preset detection sensitivity coefficient considers the instrument performance attenuation factor. The sensor is calibrated every month by using standard samples, and the sensitivity drift curve is recorded and the compensation parameters are updated. The generation process of the initial prediction value contains an outlier filtering mechanism, which uses the Isolation Forest algorithm to identify and eliminate prediction results that obviously deviate from the physical law, ensuring the reliability of the output data.

[0053] With the water quality monitoring project of a downstream river section of a chemical plant as an example, the quantum sensor array detects abnormal quantum signal response, and the system starts the pollutant quantum identification process. There is a history of benzene series and heavy metal composite pollution in this river section. The quantum excitation probe collects characteristic fluorescence signals in the 248-380 nm waveband and specific energy level transition patterns corresponding to chromium ions. The laboratory calibration database shows that the quantum fingerprint of benzene contains a fluorescence peak at 312 nm and a fluorescence lifetime characteristic of 1.8 ns, and hexavalent chromium ions have a significant absorption peak at 358 nm and a spin relaxation time. The pollutant recognition adaptive coefficients are calculated by a machine learning algorithm, with a benzene series discrimination weight of 0.76 and a heavy metal weight of 0.68. These coefficients constitute the adjustment parameters of the recognition model. The pollutant quantum recognition model is built using a convolutional neural network architecture. The network input layer is designed with 128 nodes corresponding to the frequency domain characteristic values of the quantum signal. The hidden layer contains 3 convolutional layers and 2 pooling layers for feature extraction, and the output layer uses the sigmoid function to generate the probability of the existence of pollutants. The model training uses a historical monitoring data set containing 2000 benzene series samples and 1500 heavy metal samples of quantum response data. The adaptive momentum optimization algorithm is used to adjust the network weights during the training process. The solution of the quantum state superposition equation is embedded in the feature extraction layer of the network, which maps the input quantum signal to the state vector in the high-dimensional Hilbert space. The quantum correlation strength of each pollutant is obtained by calculating the reduced density matrix.

[0054] The input quantum signal response data is standardized. The original spectral data is smoothed by Savitzky-Golay filtering and normalized to the [0,1] interval using the min-max normalization method. Missing values are completed using a time series prediction method. The numerical solution of the quantum state superposition equation is solved in real time during the model inference process. To address the temperature drift effect in water bodies, a temperature compensation algorithm is introduced to correct the calculation results. This algorithm is based on experimental data on quantum state evolution at different temperatures. When combined with the theoretical estimated value of pollutant concentration and the preset detection sensitivity coefficient, the sensitivity coefficient is dynamically adjusted according to the real-time signal-to-noise ratio. First, calculate the signal-to-noise ratio of the current quantum signal in the characteristic waveband, and obtain the basic sensitivity value through the preset mapping relationship curve. Then, based on the pH value and dissolved oxygen content of the water body, a nonlinear compensation is performed to generate the final sensitivity coefficient. The concentration fusion process uses a weighted decision algorithm to perform point multiplication between the theoretical estimated value and the sensitivity coefficient matrix, outputting the initial predicted value list of benzene series and heavy metal concentrations.

[0055] The analysis of quantum fingerprint features includes a real-time updating function that initiates a calibration procedure when an unknown fluorescence spectrum is detected: the system automatically collects water samples for gas chromatography-mass spectrometry analysis, and the identification results are stored in the database together with the quantum signal features. The update of the pollutant recognition adaptive coefficient uses an online learning mechanism, which fine-tunes the classifier parameters every week using newly collected field data to maintain the model's ability to recognize new pollutants. The training set of the quantum recognition model of pollutants contains multi-season monitoring data, and the pollutant diffusion scenarios under different flow conditions are generated by hydrological simulation software, combined with quantum simulation to generate corresponding response data, with a total of 5000 samples in the dataset. The model validation uses the leave-one-out method, with 20% of the data as the test set to evaluate the recognition accuracy of the model under different pollution levels. The solution of the quantum state superposition equation uses a numerical approximation method to discretize the continuous energy level into 64 energy level intervals, and the approximate solution of the energy eigenvalue is calculated by the variational method. The output of the theoretical concentration estimate includes spatial distribution information, and each monitoring point's concentration value is accompanied by a timestamp and coordinate marker. These data are transmitted to the data center through the Internet of Things protocol. The setting of the preset detection sensitivity coefficient takes into account the aging factor of the instrument, and the sensitivity decay curve is recorded and updated every month through standard sample calibration. The generation of initial prediction values includes a data verification step that uses the boxplot method to identify and eliminate abnormal prediction values to ensure the reasonableness of the output data.

[0056] In one embodiment, embodiment 3: generate the initial detection accuracy expectation value according to the quantitative relationship between the background interference intensity of the water area to be detected and the initial prediction value of the pollutant concentration. The calculation of the background interference intensity needs to consider multiple environmental factors: obtain the suspended solids concentration data of the water body through the turbidity sensor attached to the quantum sensor array, record the thermal fluctuation range using the temperature sensor, and input the normalized parameters into the interference evaluation model combined with the ion strength parameter measured by the conductivity sensor; this model uses a multi-dimensional linear regression algorithm to output a dimensionless index of background interference intensity, with a value range usually fluctuating between 0 and 1. The initial prediction value of the pollutant concentration comes from the output results of the aforementioned quantum recognition model, which includes a three-dimensional concentration matrix of spatial distribution. The concentration value of each grid point needs to be associated with the background interference index at the corresponding location for correlation analysis, and the correlation function takes the following form:

[0057]

[0058] where: is an intermediate variable representing the initial detection accuracy expectation value, represents the spectral interference weight of the i-th sampling point, is the predicted value of the pollutant concentration at the corresponding point, represents the temperature fluctuation variance, The variance of turbidity change is represented, and the logarithmic function is used to smooth the nonlinear impact of high concentration values. This calculation process is repeated at each time step to generate a dynamically updated precision expectation value sequence.

[0059] The construction of the quantum parameter configuration network adopts a deep reinforcement learning framework. The input layer of the network receives statistical features of the precision expectation value sequence, including mean, variance, and trend coefficient. The hidden layer contains long short-term memory units to handle time series dependencies. The output layer is mapped to the parameter space through a fully connected neural network. The network training uses a historical operation dataset that records the correspondence between precision expectation values and optimal parameters in past detection tasks. The network weights are optimized through a policy gradient algorithm. The calculation of the initial excitation power parameter is based on the principle of energy conservation, which converts the precision expectation value into the requirement for electromagnetic field strength, and then converts it into the power value according to the probe characteristic curve, usually in the order of milliwatts. The determination of the initial sampling frequency parameter needs to consider the signal attenuation characteristics. Higher precision expectation values require closer sampling intervals to capture rapidly changing quantum state information. The frequency range is usually adjusted between kilohertz and megahertz. The real-time inference process of the quantum parameter configuration network includes multiple check mechanisms. When receiving a new precision expectation value input, the network first performs input validity testing to eliminate abnormal values that are obviously beyond the physical range. Then, preliminary parameter suggestions are calculated through forward propagation. These suggestions need to be reviewed by the physical constraint module to ensure that they do not exceed the hardware limits of the instrument. The generation of the excitation power parameter also considers energy efficiency optimization factors. An adaptive particle swarm optimization algorithm is used to find the lowest power setting that meets the precision requirements, extending the field working time of the sensor array. The determination of the sampling frequency parameter also considers data storage and transmission limitations. Through the theory of compressed sensing, the sampling strategy is optimized to minimize data volume while ensuring information integrity.

[0060] The modeling of the quantitative relationship between background interference intensity and pollutant concentration adopts a dynamic updating mechanism. The system regularly collects new environmental monitoring data, recalibrates the regression model coefficients, and maintains the accuracy of interference assessment. The calculation process of precision expectation value is embedded in a real-time quality control loop, and each calculation result is compared with recent historical values for consistency. When there is a significant deviation, an artificial review process is triggered. The training data of the quantum parameter configuration network is continuously expanded, and the operation parameters and actual effects of each successful detection task are recorded and added to the training set, allowing the network parameter recommendation capability to improve over time. The specific assignment process of the initial excitation power parameter includes safety protection logic. The system queries the probe model database to obtain the maximum allowed 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 upper limit of the processor load, the downsampling algorithm is automatically enabled, and the precision expectation value is adjusted synchronously. All parameter generation log records detailed calculation process and decision basis. These log files are transmitted and stored to the central server through encryption, which is used for subsequent algorithm auditing and optimization. The process forms an automated pipeline from environmental perception to parameter generation. The calculation of background interference intensity is updated every five minutes, the recalculation period of precision expectation value is one minute, and the inference process of parameter configuration network is completed within seconds. This design ensures that the detection system can quickly respond to environmental changes and adjust the instrument settings in time to obtain the best detection effect. The parameter generation module and the hardware control unit interact with standardized communication protocols. The generated parameter set is sent to each quantum detection probe through digital instructions, and the microcontroller inside the probe adjusts the working state according to the instructions.

[0061] In one embodiment, example 4: After the quantum detection instrument performs the initial pollution detection action, the system starts the collection process of the actual pollutant concentration data: taking a monitoring point in a certain river basin as an example, the deployed quantum sensor array covers a 200-meter river section, containing 12 probe nodes; the initial detection uses the initial parameter setting, the excitation power is 85 milliwatts, and the sampling frequency is 120 kilohertz. After the detection is completed, the water samples collected by each probe node are analyzed by a portable mass spectrometer in the laboratory to obtain the actual pollutant concentration data, and the latitude and longitude coordinates and water depth information of the collection point are recorded. These measured data are sent to the central processing unit through the wireless transmission module, and are spatially matched and time-aligned with the previously generated initial predicted value of the pollutant concentration. The comparison process uses a multi-dimensional error analysis algorithm to divide the monitoring area into 50x50 meter grid cells, and calculates the absolute and relative deviations of the predicted value and the measured value in each cell; the system pays special attention to the comparison results of the downstream areas of key pollution sources, which usually have more complex diffusion patterns. The determination of detection error considers both spatial distribution characteristics and temporal volatility. It not only calculates the overall average error, but also statistics the error dispersion and spatial autocorrelation, and finally generates an evaluation report containing error value and confidence interval.

[0062] The operation of the quantum parameter adaptive algorithm is based on the error gradient analysis principle. The instrument performance parameter database is loaded during the initialization of the algorithm, including the power adjustment range and frequency response characteristics of different types of probes. The algorithm core uses the perturbation observation method to observe the error trend by adjusting the parameter value slightly, and calculates the correction direction and amplitude of the power and frequency. The calculation of the excitation power correction considers the energy transmission efficiency factor, and the adjustment step is determined according to the error size and sign. Negative error (measured value is lower than predicted value) usually requires increasing the excitation intensity, and positive error correspondingly reduces the power output. The determination of the sampling frequency correction quantity synchronously analyzes the signal decay curve. Higher error areas often need to improve the sampling density to capture more detailed quantum state evolution information. The application of the correction quantity is realized through the parameter mapping function, the function input is the original parameter and the correction quantity, and the output is the optimized parameter value; the power correction adopts linear superposition, and the frequency correction considers logarithmic scale to adapt to the order of magnitude change. The calculation result of the optimized excitation power parameter needs to pass through the safety verification module to ensure that it does not exceed the maximum bearing power of the probe; the optimized sampling frequency parameter needs to verify the compatibility with the data acquisition system to avoid exceeding the processor cache capacity. After the parameter optimization is completed, a new detection instruction set is generated, including the updated power value, frequency value and corresponding timing control information.

[0063] The reprocessing process of the quantum parameter configuration network introduces a multi-objective optimization strategy. The network receives the optimized power and frequency parameters as input, evaluates the comprehensive performance of the parameter combination in terms of energy consumption, accuracy and stability, and calculates the Pareto frontier of different parameter combinations through the game decision algorithm of the network hidden layer to select the optimal balance point. The final output format of the optimized detection parameters includes binary control instructions and human-readable configuration files. The control instructions are issued to the probe controller through the industrial Ethernet protocol, and the configuration files are archived for audit tracking. The execution of the secondary pollution detection action adopts a phased starting strategy. First, the three grid areas with the largest error are selected for pilot detection, and then the parameters are gradually expanded to the whole area after verifying their effectiveness. Real-time monitoring of signal quality indicators is performed during the detection process, and an adaptive degradation mechanism is triggered when abnormal noise occurs. After the detection is completed, the pollutant concentration distribution data collected is used to generate a spatial contour map through the Kriging interpolation method, and the hot spot area and concentration gradient change direction are marked on the map.

[0064] The detection efficiency evaluation adopts a multi-index comprehensive analysis method, mainly considering the data quality improvement degree before and after parameter adjustment, the total time consumption change of the detection task, and the system resource consumption situation; the evaluation results are presented in the form of structured reports, including quantitative indicators and qualitative analysis opinions. The whole correction and optimization process forms a closed-loop feedback loop. The error data of this detection is automatically added to the training database to improve the accuracy of the subsequent parameter recommendation algorithm. See Table 1.

[0065] Table 1: Initial detection error analysis sample data

[0066]

[0067] The error analysis process found that the system bias pattern in certain areas, the grid points located at the river bends generally showed positive error characteristics (measured values were higher than predicted values), while the straight river sections showed negative error trends; this spatial distribution characteristic was incorporated into the correction amount calculation algorithm, and different correction strategies were used for different terrain areas. The power correction amount allocation considered the probe spacing factor, and the nodes located at the array edge were given a larger adjustment margin, while the center nodes used a relatively conservative correction amplitude. The frequency correction amount was set in relation to the water flow rate, and the sampling frequency was increased in areas with higher flow rates to compensate for the time delay effect during signal transmission. The verification of the optimized detection parameters used the cross-validation method, dividing the monitoring area into a training set and a test set to ensure the applicability of the parameters under different geographical conditions. The data collection of the secondary detection action increased the recording of auxiliary environmental parameters, including real-time flow rate, flow direction, and air temperature data, which were used for subsequent analysis of the influence of environmental factors on detection accuracy. The final output of the pollutant concentration distribution map included an uncertainty evaluation layer, which used color depth to represent the measurement confidence level of each point.

[0068] In one embodiment, embodiment 5: after obtaining the initial predicted value of the pollutant concentration of the water area to be detected, the system starts the fusion verification process of the multispectral quantum response data: taking the monitoring of the downstream river section of a certain chemical industrial park as an example, the quantum sensor array synchronously collects quantum response data in the ultraviolet waveband (250-400 nm), visible light waveband (400-700 nm), and near-infrared waveband (700-1100 nm). After preprocessing, the data in each waveband is input into the corresponding spectral analysis module. The ultraviolet waveband focuses on detecting the fluorescence signal generated by aromatic organic matter, the visible light waveband analyzes the scattering effect caused by turbidity, and the near-infrared waveband captures the vibration absorption characteristics of water molecules. The cross-validation uses a decision-level fusion strategy, and the concentration estimates obtained from the three wavebands are input into the D-S evidence theory model to calculate the consistency and conflict of the predicted values at each point. When the conflict exceeds the threshold, the re-detection mechanism is triggered. During the fusion process, a spectral reliability weight matrix is established, and the weight values are dynamically adjusted according to the signal-to-noise ratio and historical accuracy of each waveband signal. The weight coefficient of the ultraviolet waveband is usually set to 0.35-0.45, the weight of the visible light waveband is 0.25-0.35, and the weight of the near-infrared waveband is 0.20-0.30. The verification result generates a spatial consistency distribution map, which marks the matching degree of the multispectral verification result and the initial predicted value for each grid element. Areas with a matching degree lower than 85% are automatically marked as areas to be reviewed.

[0069] Based on the first deviation data, the sampling interval and signal acquisition sensitivity of the quantum sensor array are dynamically adjusted, and the deviation distribution pattern found in the initial detection is analyzed: for the area with absolute deviation exceeding 15 μg / L, the sampling interval is shortened from the default 5 minutes to 2 minutes; for the area with deviation between 5-15 μg / L, the sampling interval is adjusted to 3 minutes; for the area with deviation below 5 μg / L, the original sampling interval is maintained. The adjustment of signal acquisition sensitivity adopts a hierarchical strategy, with the probe gain increased by 3 dB in high deviation areas, 1.5 dB in medium deviation areas, and the original setting maintained in low deviation areas. The adjustment algorithm considers the probe power consumption constraint, and automatically enables the energy saving mode when the battery power is below 50%, reducing the adjustment amplitude by a certain proportion. The dynamic adjustment of the sampling interval introduces a sliding window mechanism, and the system calculates the moving average of the deviation of the last 10 sampling periods in real time, and triggers the early warning interval compression when the average value continues to rise. The adjustment of signal acquisition sensitivity also considers the ambient light conditions, with an automatic increase of 0.5 dB gain at night to compensate for environmental noise, and a corresponding decrease in gain under strong light conditions to prevent signal saturation. All adjustment parameters are sent to the probe node through encrypted data packets, and the node controller returns an execution status code after confirming the validity of the parameters.

[0070] Before performing the secondary pollution detection action, the system starts the real-time monitoring and parameter correction program of the water body temperature change rate: a high-precision temperature sensor (measurement accuracy ±0.1℃) is integrated in each probe node, and water temperature data is collected every 30 seconds, and the absolute value of the temperature change rate per minute is calculated. The correction of the quantum state excitation threshold value adopts a temperature compensation algorithm, and a threshold adjustment function with temperature as the independent variable is established, and the threshold value is reduced by 0.5 mV for every 1℃ rise in temperature, and the threshold value is increased by 0.5 mV for every 1℃ drop in temperature. The correction process introduces an inertial delay mechanism, when the temperature change rate exceeds 0.5℃ / minute, the correction is delayed for 60 seconds to avoid excessive adjustment. The threshold correction parameters are generated through distributed calculation, each probe node independently calculates the local correction value and uploads it to the central node for consistency verification, when the correction values of adjacent nodes differ by more than 15%, the collaborative calibration process is triggered. The final determination of the optimized detection parameters adopts a voting mechanism, the correction scheme agreed by more than two-thirds of the nodes is adopted and executed. The corrected parameter set includes the updated excitation threshold, power parameter and frequency parameter, which are packaged into a detection instruction set and sent to all probe nodes.

[0071] The processing of multispectral verification data includes an outlier identification link. When the data of a certain waveband deviates significantly from other wavebands, the system automatically checks the calibration state of the waveband sensor and triggers the online calibration program if necessary. All parameter changes during dynamic adjustment are recorded in the operation log, including adjustment time, adjustment amplitude, adjustment reason and execution effect. These logs are used for subsequent optimization of the adjustment algorithm. The temperature correction program includes a failsafe mechanism. When the temperature sensor reading abnormally fluctuates, it automatically switches to the last known normal value to avoid false correction leading to detection failure. Multispectral data verification ensures data reliability from the spatial dimension, dynamic adjustment mechanism improves monitoring adaptability from the time dimension, and real-time temperature correction enhances system robustness from the environmental dimension. All operations are automatically executed in the background without human intervention, and an alarm message is sent to the monitoring platform only when a major anomaly is detected.

[0072] The above only describes the preferred embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the specification and drawings, are also included in the patent protection scope of the present application.

Claims

1. A method for rapid detection of water pollution driven by quantum sensing technology, characterized in that, The method comprises: 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; constructing a quantum recognition model of pollutants in the water area to be detected, inputting the quantum signal response data into the quantum recognition model of pollutants, and obtaining an initial predicted value of the pollutant concentration in the water area to be detected; generating initial detection parameters of a quantum detection instrument based on the initial predicted value of the pollutant concentration; collecting first deviation data of actual detection values and expected detection values after the quantum detection instrument performs a first pollution detection action based on the initial detection parameters; determining a detection error based on the first deviation data of the actual detection values and the expected detection values, correcting the initial detection parameters based on the detection error, and generating optimized detection parameters of the quantum detection instrument; the quantum detection instrument performs a second pollution detection action based on the optimized detection parameters; the initial detection parameters of the quantum detection instrument based on the initial predicted value of the pollutant concentration comprise: generating an initial detection accuracy expectation value according to the quantitative relationship between the background interference intensity of the water area to be detected and the initial predicted value of the pollutant concentration, and generating initial excitation power parameters and initial sampling frequency parameters of the quantum detection instrument according to the initial detection accuracy expectation value; the initial excitation power parameters and the initial sampling frequency parameters of the quantum detection instrument generated according to the initial detection accuracy expectation value comprise: establishing a quantum parameter configuration network, taking the initial detection accuracy expectation value as an input variable, and calculating and obtaining the initial excitation power parameters and the initial sampling frequency parameters of the quantum detection instrument; the detection error is determined based on the first deviation data of the actual detection values and the expected detection values, the initial detection parameters are corrected based on the detection error, and the optimized detection parameters of the quantum detection instrument are generated, which comprises: collecting actual pollutant concentration data of the water area to be detected after the quantum detection instrument performs a first pollution detection action, and comparing the actual pollutant concentration data with the initial predicted value of the pollutant concentration to determine the detection error; using a quantum parameter adaptive algorithm to analyze the detection error, generating an excitation power correction amount and a sampling frequency correction amount of 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 modified to generate optimized excitation power parameters and optimized sampling frequency parameters; the optimized excitation power parameters and the optimized sampling frequency parameters are processed according to the quantum parameter configuration network to generate the optimized detection parameters of the quantum detection instrument.

2. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The quantum sensor array is deployed in the water area to be detected, and the quantum signal response data in the water area to be detected is collected, which comprises: deploying multiple groups of quantum excitation probes in the water area to be detected, and collecting quantum state transition response sequences of the water area to be detected within a fixed monitoring period; analyzing the water flow characteristics and potential pollution source distribution of the water area to be detected to obtain spectral interference weights of different pollution sources; based on the quantum state transition response sequences and the spectral interference weights of different pollution sources, a quantum signal propagation topology model of the water area to be detected is constructed, and quantum signal response data of the water area to be detected is extracted according to time nodes.

3. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The pollution quantum recognition model of the water area to be detected is constructed, and the quantum signal response data is input into the pollution quantum recognition model to obtain an initial prediction value of the pollution concentration of the water area to be detected. The quantum fingerprint characteristics of the known pollutants in the water area to be detected are analyzed, an adaptive coefficient of pollution recognition is obtained, and a pollution quantum recognition model is built. The quantum signal response data is input into the pollution quantum recognition model, and a theoretical estimated value of the pollution concentration is obtained by solving a quantum state superposition equation. The initial prediction value of the pollution concentration of the water area to be detected is obtained by combining the theoretical estimated value of the pollution concentration and a preset detection sensitivity coefficient.

4. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The quantum detection instrument performs a secondary pollution detection action based on the optimized detection parameters, and the method further comprises the following steps. After the secondary pollution detection action is completed, pollution concentration distribution data of the water area to be detected after the secondary detection is completed is collected, and the detection efficiency of the secondary pollution detection action is evaluated.

5. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The method further comprises the following steps. After the initial prediction value of the pollution concentration of the water area to be detected is obtained, the initial prediction value of the pollution concentration is cross-validated by fusing multispectral quantum response data.

6. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The method further comprises the following steps. Based on the first bias data, the sampling interval and the signal acquisition sensitivity of the quantum sensor array are dynamically adjusted.

7. The quantum sensing technology driven rapid detection method for water quality pollution according to claim 1, characterized in that, The method further comprises the following steps. Before the secondary pollution detection action is performed, the quantum state excitation threshold in the optimized detection parameters is corrected in real time according to the water temperature change rate.

Images