A method and system for detecting pathogenic organisms in a body of water

Abstract

This invention discloses a method and system for detecting pathogenic organisms in aquatic bodies, belonging to the field of biological detection technology. Based on historical hydrological data and meteorological forecasts, a spatiotemporal distribution model of microbial concentration is constructed to generate a set of coordinates for suspected high-risk areas for pathogens. An unmanned surface vessel (USV) automatically cruises according to the coordinates and conducts water sampling and membrane enrichment, while simultaneously monitoring water temperature, pH, turbidity, and enrichment membrane pressure difference in real time, dynamically determining whether to terminate sampling. After the enriched samples are activated by electric field, magnetic field, or temperature control, real-time quantitative PCR is performed to obtain Ct values ​​at each location, calculate the offset density cumulative index, and identify areas with strong positive signals. Finally, based on the Ct value distribution and prediction results, a spatial distribution map and multi-level concentration map of pathogenic organisms are output for water quality risk assessment and emergency response. This invention has highly automated, spatially accurate, and intelligent analysis capabilities, and is suitable for pathogen monitoring and dynamic early warning in large-scale complex water bodies.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, specifically to a method and system for detecting pathogenic organisms in water. Background Technology

[0002] Pathogens in aquatic bodies (such as bacteria, viruses, and parasites) are a significant factor in the outbreak of infectious diseases, especially in urban drinking water sources, nature reserves, aquaculture areas, and tourist waterways, where their concentration and distribution directly affect public health and ecological health. Traditional detection methods mainly rely on manual sampling combined with laboratory culture or molecular biological analysis, but these methods have the following serious drawbacks in practice:

[0003] The concentration of target microorganisms is extremely low, making them difficult to capture effectively: many pathogenic microorganisms have concentrations in water as low as [missing information]. The CFU / L concentration is far below the direct detection threshold of the instrument, requiring prior high-level enrichment.

[0004] Complex water backgrounds can interfere with detection: Natural water bodies contain complex components such as dissolved organic matter, suspended particles, and algae, which can easily cause an inhibitory effect on molecular detection methods such as PCR, affecting specificity and sensitivity.

[0005] Lack of spatial resolution in sampling: Currently, most sampling points are set up randomly or empirically, which cannot depict the spatial diffusion trend and concentration gradient of pathogens, resulting in a lag in prevention and control deployment.

[0006] Furthermore, traditional sampling methods are blind and cannot be used to target and dynamically adjust enrichment strategies based on the migration and diffusion patterns of microorganisms in water.

[0007] Therefore, there is an urgent need for a detection method that integrates aquatic microbial enrichment, spatiotemporal identification, signal enhancement and intelligent detection, which can achieve accurate, dynamic and in-situ detection of low concentrations of aquatic pathogens, especially suitable for small pathogen targets in complex natural aquatic scenarios, such as parasite eggs, virus particles or conditional pathogens. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for detecting pathogenic organisms in water bodies, in order to address the shortcomings of the prior art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting pathogenic organisms in aquatic bodies, comprising:

[0010] S100. Obtain historical hydrological data and seasonal microbial distribution models for the target water body area, and combine them with meteorological forecast data to generate a set of coordinates P of high-risk areas of suspected pathogens at multiple locations in the water body.

[0011] S200: Based on the coordinate set P of the high-risk area, control the unmanned surface vessel to autonomously cruise to each target point, and initiate water sampling and microbial enrichment at each point;

[0012] S300. During the enrichment process, record the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane at each point, and determine whether ΔP exceeds the preset threshold Pmax. If so, stop the current sampling and move to the next point; otherwise, continue sampling until the set volume is reached.

[0013] S400. After enriching the microbial samples at each site, perform molecular activation treatment induced by electric field / magnetic field or temperature control, and perform real-time quantitative PCR reaction on the treated samples to record the Ct value of each site.

[0014] S500: Based on the Ct value distribution, determine whether there is a region with a strong positive signal. If so, automatically return to the region and perform multiple repeated sampling verifications.

[0015] S600. Based on the verification results, output the final spatial distribution map and concentration level map of pathogens in the target water body to guide on-site water quality risk assessment and emergency response deployment.

[0016] Preferably, the step of generating the set of coordinates P of high-risk areas for suspected pathogens at multiple locations in the water body includes:

[0017] Acquire historical hydrological data of the target water body over the past period, including water flow velocity, water temperature, rainfall and sunshine duration, and perform time-series normalization on the historical hydrological data;

[0018] Based on historical hydrological data, a spatiotemporal distribution prediction model for pathogen concentration was constructed, and a support vector regression algorithm was used to establish the fitting relationship between various hydrological factors and pathogen concentration.

[0019] By combining current meteorological forecast data and modeling results, the pathogen concentration risk value in each grid cell of the target water body is calculated, and a risk heat map is obtained.

[0020] Grid cells in the risk heatmap where the pathogen concentration exceeds a set concentration threshold are extracted as high-risk areas, and corresponding geographic coordinate points are generated to form a high-risk area coordinate set P.

[0021] Preferably, the steps for controlling the unmanned surface vessel to autonomously cruise to various target locations based on the coordinate set P of the high-risk area include:

[0022] The coordinate points in the high-risk area coordinate set P are sorted according to the shortest navigation distance principle to generate a continuous cruise path, and the sorting result is converted into a geographic coordinate navigation command sequence that can be recognized by the unmanned surface vessel.

[0023] After receiving the navigation command sequence, the unmanned surface vessel (USV) uses satellite positioning data and heading sensor data to calculate in real time the offset distance and heading deviation between the current position of the USV and the target point.

[0024] When the distance between the unmanned surface vessel and the target location is less than the preset arrival judgment radius, it automatically reduces its propulsion speed and enters a fixed-point navigation state.

[0025] After completing the stay at the current location, mark the current location as a completed sampling point.

[0026] Preferably, the step of determining whether the pressure difference ΔP across the enrichment membrane exceeds a preset threshold Pmax includes:

[0027] A first pressure sensor and a second pressure sensor are respectively installed at the inlet and outlet of the enrichment membrane to obtain the inlet water pressure P1 and the outlet water pressure P2 in real time.

[0028] Simultaneous sampling of P1 and P2 was performed at fixed time intervals, and the pressure difference across the enrichment membrane was calculated. ;

[0029] Will Compare with the set differential pressure threshold Pmax, if If the value is greater than or equal to Pmax, it is determined that the enrichment membrane is showing signs of clogging, and the sampling device is controlled to stop pumping water and generate an alarm signal.

[0030] Preferably, based on the Ct value distribution, the presence of a strong positive signal region is determined, including:

[0031] The cyclic threshold Ct values ​​of all sampling points are mapped to a two-dimensional spatial grid according to geographic coordinates, and the spatial gradient variance exponent of Ct values ​​between each point and its adjacent points is calculated.

[0032] Set a positive threshold for Ct value, extract all points with Ct values ​​less than the positive threshold as suspected positive points, and generate a cumulative Ct value offset density index in their local neighborhood.

[0033] The spatial gradient variance index of Ct values ​​and the cumulative index of Ct value offset density are jointly analyzed;

[0034] If the spatial gradient variance index of Ct value and the cumulative index of Ct value offset density both exceed the preset threshold in a certain region, the region is determined to be a positive strong signal region.

[0035] Preferably, the step of generating the Ct value offset density accumulation index includes:

[0036] The geographic coordinates of each sampling point are used as nodes in the graph structure, and adjacency relationships are constructed based on the spatial distance between the points. When the distance between points is less than the set neighborhood radius, a connecting edge is established, thereby forming a spatial association graph structure.

[0037] The cyclic threshold Ct value and the density of positive points in the local neighborhood of each node are used as the node features input to the graph neural network.

[0038] The spatial clustering intensity value of each node is calculated based on the node feature vector after propagation, and then weighted and accumulated in combination with the original Ct value offset degree to obtain the Ct value offset density cumulative index of each sampling point.

[0039] Preferably, the step of outputting the final spatial distribution map and concentration level map of the pathogen in the target water body based on the verification results includes:

[0040] Input the cyclic threshold Ct values ​​and their corresponding coordinates of all sampling points into the spatial modeling module, construct the spatial variogram model of Ct values ​​using the ordinary Kriging interpolation algorithm, and determine the optimal fitting semi-variogram type and parameter set.

[0041] Based on the modeling results, the predicted Ct values ​​and prediction errors of all unsampled grid points in the target water body region are calculated to obtain a complete spatial continuous concentration distribution dataset.

[0042] The predicted Ct value is compared with the set positive threshold, the probability that the Ct value of each grid point is less than the positive threshold is calculated, and the positive probability distribution map is output.

[0043] Based on the predicted Ct values ​​and positive probability results, a spatial distribution map and multi-level concentration map of pathogens in the target water body are generated.

[0044] The present invention also provides an aquatic pathogen detection system, comprising:

[0045] High-risk identification module: acquires historical hydrological data and seasonal microbial distribution models of the target water body area, and combines them with meteorological forecast data to generate a set of coordinates P of high-risk areas of suspected pathogens at multiple locations in the water body;

[0046] Path planning module: Based on the coordinate set P of high-risk areas, control the unmanned surface vessel to autonomously cruise to each target point, and initiate water sampling and microbial enrichment at each point;

[0047] Dynamic judgment module: During the enrichment process, the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane are recorded at each point, and it is determined whether ΔP exceeds the preset threshold Pmax. If so, the current sampling is stopped and the process moves to the next point; otherwise, sampling continues until the set volume is reached.

[0048] Detection module: The enriched microbial samples at each site are subjected to molecular activation treatment induced by electric field / magnetic field or temperature control, and the treated samples are subjected to real-time quantitative PCR reaction, and the Ct value of each site is recorded.

[0049] Anomaly detection module: Based on the distribution of Ct values, determine whether there are areas with strong positive signals. If so, automatically return to the area and perform multiple repeated sampling verifications.

[0050] Visualization output module: Based on the verification results, outputs the final spatial distribution map and concentration level map of pathogens in the target water body, which can be used to guide on-site water quality risk assessment and emergency response deployment.

[0051] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0052] 1. This invention, by integrating multi-source hydrological and meteorological data modeling, graph neural network analysis, high-precision navigation control, and in-situ nucleic acid detection, achieves for the first time a closed-loop control process encompassing the entire workflow from identifying high-risk pathogen areas, automatic enrichment sampling, and intelligent blockage monitoring, to microbial activation, real-time detection, and spatial distribution mapping. Compared to traditional methods relying on manual sampling and laboratory analysis, this invention offers advantages such as high automation, strong real-time performance, and clear spatial targeting, significantly improving the efficiency, accuracy, and responsiveness of water pathogen monitoring.

[0053] 2. The spatial clustering model of Ct values ​​and the offset density accumulation index constructed in this invention, combined with the feature propagation capability of graph neural networks, can accurately identify local enrichment areas of low-concentration pathogens and realize dynamic sampling point revisiting and intelligent judgment, effectively avoiding missed detections and misjudgments before pathogen outbreaks. Its output spatial distribution map and concentration level map can directly serve smart water management platforms, drinking water source protection, and public health emergency systems, demonstrating significant engineering application value and promising prospects for widespread application. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0055] Figure 1 This is a flowchart of the method of the present invention.

[0056] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1, please refer to Figure 1 As shown in this embodiment, a method for detecting pathogenic organisms in water includes:

[0059] S100. Obtain historical hydrological data and seasonal microbial distribution models for the target water body area, and combine them with meteorological forecast data to generate a set of coordinates P of high-risk areas for suspected pathogens at multiple locations in the water body.

[0060] In this invention, to determine the high-risk distribution areas of pathogenic microorganisms in water bodies, a high-risk coordinate set P is generated for unmanned surface vessel path planning and enrichment sampling, specifically including the following steps:

[0061] First, historical hydrological parameter data for 30 consecutive days is acquired from online monitoring equipment and meteorological stations deployed in the target water body area. These parameters include:

[0062] Water flow velocity: The daily average flow velocity was recorded using a Doppler current meter, and the unit is meters per second;

[0063] Water temperature: The daily average temperature is recorded using temperature sensors, and the unit is degrees Celsius.

[0064] Rainfall data: provided by hydrological and meteorological stations, in millimeters per day;

[0065] Sunshine duration: The effective daily sunshine duration is estimated using a total radiometer, and the unit is hours / day.

[0066] To eliminate the interference caused by the differences in the dimensions of the parameters in the modeling, a linear minimum-maximum normalization algorithm is used to process each time series. The normalization method is as follows:

[0067] Convert each parameter x as follows: : Where xmin and xmax are the minimum and maximum values ​​of this parameter over 30 days, respectively. The value is normalized to between 0 and 1.

[0068] After normalizing the hydrological data, the normalized data was paired with measured pathogen concentration data collected synchronously in the same water area within 30 days for supervised learning modeling. Pathogen concentration was expressed in CFU (colony forming units) per liter. Support vector regression was used as the modeling method, with the following steps:

[0069] Define the input feature vector X=[vt, Tt, rt, st], which represent the normalized water flow velocity, water temperature, rainfall, and sunshine duration for the day, respectively.

[0070] The output target value is yt, which represents the actual concentration of pathogenic microorganisms measured on that day;

[0071] Use the radial basis function (RBF) as the kernel function;

[0072] The optimal combination of penalty parameter C and kernel width σ is determined by grid search method, with the objective of minimizing the mean absolute error.

[0073] Using 70% of the samples as the training set and 30% as the validation set, we performed five-fold cross-validation to ensure the robustness of the model under different sample distributions.

[0074] The trained nonlinear prediction model f(X) is obtained and used to predict the pathogen concentration corresponding to unknown inputs.

[0075] The meteorological forecast data (including water flow velocity, temperature, rainfall and sunshine) for the next 7 days is used as input and fed into the above prediction model f(X) day by day to calculate the predicted concentration of pathogenic microorganisms in each geographic grid unit in the target water area.

[0076] Specifically, the target water area is divided into several square grid cells with a side length of 50 meters according to latitude and longitude. The center coordinates of each cell are (xi, yi). For each coordinate point, the model is called to calculate the corresponding pathogen concentration ci = f(Xi). The ci values ​​of all grids are rendered into a two-dimensional heat map in the form of color gradients to form a complete risk map.

[0077] To define "high-risk" areas, a concentration risk threshold R is set. This threshold R is set according to the environmental alert standards for the target pathogen type. For example, when the target is an amoebae or cryptosporidia, based on references and water quality standards, it is preferably set to 1000 CFU / L.

[0078] Traverse all grid cells in the heatmap, filter out all coordinate points (xi, yi) that satisfy the condition ci ≥ R, and collect these coordinate points into set P. Set P is the set of points in the suspected high-risk area of ​​pathogens, which will be used in the subsequent path planning module of the unmanned surface vessel for targeted enrichment sampling.

[0079] Through the above steps, combining historical evolution trends with future dynamic predictions, high-risk area identification based on multi-factor dynamic extrapolation was achieved. Compared with traditional static sampling methods, this method can achieve early warning, accurate source tracing, and dynamic updates, providing a scientific and reasonable basis for the deployment of sampling points for water pathogen detection.

[0080] S200: Based on the coordinate set P of the high-risk area, control the unmanned surface vessel to autonomously cruise to each target location, and initiate water sampling and microbial enrichment at each location.

[0081] In this invention, to achieve accurate sampling of suspected high-risk areas for pathogens in water bodies by unmanned surface vessels (USVs), path planning and autonomous navigation of the USV are required based on the generated coordinate set P of the high-risk areas. The control process includes the following steps:

[0082] First, the multiple two-dimensional geographic coordinate points (represented in latitude and longitude) contained in the high-risk area coordinate set P are input into the path optimization algorithm to perform path sorting. Preferably, an improved nearest neighbor heuristic algorithm is used. Starting from the unmanned surface vessel's (USV) starting position, the Euclidean distance from the current point to all unvisited points in set P is calculated, and the point with the shortest distance is selected as the next cruise target. This process continues until all target points are sorted. The algorithm implementation steps are as follows:

[0083] The initial path list is empty, and the current point is set as the starting coordinate.

[0084] Find the next coordinate in the unsorted set of points that has the smallest distance to the current point;

[0085] Add the coordinates to the path list and remove them from the unsorted set;

[0086] Update the current point to the newly selected point, and repeat steps 2 to 3 until set P is empty.

[0087] After sorting, an ordered coordinate sequence {P1,P2,...,Pn} will be obtained. This sequence will then be formatted into a navigation command sequence containing timestamps, latitude and longitude, and expected dwell time, and stored in the unmanned surface vessel control chip for subsequent execution.

[0088] During the patrol mission, the unmanned surface vessel obtains the real-time latitude and longitude coordinates of the hull through the built-in global navigation satellite system receiver, while the heading sensor installed on the hull provides real-time feedback on the hull's orientation (angle based on north, in degrees).

[0089] The control program calculates two key navigation parameters between the current location and the target point every second:

[0090] Position offset distance D: The geodetic distance between the current coordinates and the target point coordinates is calculated using the Havesing formula, and the unit is meters;

[0091] Heading deviation angle θ: The angle between the hull's orientation and the direction of the line connecting the target point, calculated using inverse trigonometric functions, is expressed in degrees.

[0092] Based on the sign of the heading deviation θ, the control program adjusts the power difference between the left and right thrusters, employing a differential speed control algorithm to correct the heading. For example, when θ is positive, indicating the hull is veering to the right, the power of the left thruster is maintained while the power of the right thruster is reduced; when θ is negative, the opposite is true. This cyclical correction ensures the unmanned surface vessel consistently propels stably toward the target direction.

[0093] To achieve stable sampling of the target location, the system sets a arrival threshold Rd, preferably 5 meters. When the positional offset D between the unmanned surface vessel and the target location is less than or equal to Rd, the target location is considered to have been reached. At this point, the following operations are performed:

[0094] Reduce the power of both thrusters to 30% of rated output;

[0095] The heading sensor is used to continuously monitor the rate of change of the hull rotation angle.

[0096] If the angular velocity is within ±1 degree per second and the duration exceeds 5 seconds, the hull is determined to be stationary, triggering the sampling action.

[0097] By combining the above judgments, it is ensured that the unmanned surface vessel does not deviate due to inertia or disturbance, thus meeting the prerequisite for accurate enrichment sampling.

[0098] After completing the stationary stop and sampling actions at the current location, the system marks the current coordinate point as "completed" and reads the coordinates of the next location from the navigation command sequence as the new navigation target. The navigation control program then re-executes the offset and heading calculation logic described in step two, driving the unmanned surface vessel into the next navigation cycle.

[0099] When all target points in the navigation command sequence have been completed or the mission interruption condition (such as the battery level being lower than a set threshold) is triggered, the system will automatically terminate the path execution and return to the starting position.

[0100] Through the above steps, this invention combines data-driven high-risk coordinates with real-time autonomous navigation, enabling unmanned surface vessels to have the ability to navigate with optimal paths, autonomous decision-making, and precise positioning, thus significantly improving the efficiency and scientific rigor of aquatic pathogen sampling.

[0101] S300. During the enrichment process, record the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane at each point, and determine whether ΔP exceeds the preset threshold Pmax. If so, stop the current sampling and move to the next point; otherwise, continue sampling until the set volume is reached.

[0102] In this invention, to ensure the stability of the enrichment process and prevent sampling interruptions or decreased biological retention efficiency due to enrichment membrane blockage, the system simultaneously performs environmental parameter acquisition and differential pressure judgment operations during the enrichment process at each sampling point, specifically including the following steps:

[0103] During the enrichment process at each sampling point, the following physicochemical parameters of the water body are collected in real time using sensors placed near the sampling pipeline:

[0104] Water temperature: The real-time temperature value of the sampled water body is obtained through a thermistor temperature sensor, in degrees Celsius;

[0105] pH: The pH of the water body is measured in real time using a glass electrode pH probe;

[0106] Turbidity: An infrared light scattering turbidity sensor was used, and the measurement unit was formalin turbidity unit (NTU).

[0107] Each parameter is recorded once with a time step of 1 second. All data is bound to the geographic coordinates and timestamp of the current sampling point and stored in the sampling controller for subsequent data analysis and dynamic pollution map drawing.

[0108] To monitor the working status of the enrichment membrane, a first pressure sensor and a second pressure sensor are installed at the inlet and outlet of the enrichment unit, respectively, to measure the pressure of water entering and passing through the enrichment membrane in real time, denoted as P1 and P2, respectively, in kilopascals.

[0109] Instantaneous readings of P1 and P2 are collected every 2 seconds, and the current pressure difference ΔP is calculated using the formula: Pressure difference ΔP equals inlet water pressure P1 minus outlet water pressure P2. That is: ΔP = P1 - P2. The pressure difference ΔP reflects the water flow resistance state inside the enrichment membrane; a larger value indicates more severe membrane clogging. A pressure difference threshold Pmax is set for judging enrichment membrane blockage, preferably 30 kPa. This value is determined based on the inflection point of enrichment efficiency decline in experiments; that is, when ΔP exceeds 30 kPa, the enrichment efficiency decreases significantly, and there is a risk of membrane structure damage.

[0110] After each calculation of ΔP, it is immediately compared with Pmax: if ΔP is greater than or equal to Pmax, it is determined that the current enrichment membrane has a tendency to become clogged, and the following operations are immediately performed: stop the sampling pump and terminate the current water extraction process; send a clogged alarm signal to the main controller; record the coordinates, time, and ΔP value of the current point as an "abnormal sampling record"; automatically load the next target sampling point and enter the cruise phase;

[0111] If ΔP is less than Pmax, the membrane is considered to be unobstructed, and the enrichment operation continues until the water sampling volume at the current point reaches the preset value V (e.g., 1 liter), at which point the sampling is terminated and the path is switched.

[0112] Through the above design and judgment logic, this invention not only achieves complete recording of aquatic environmental parameters, but also effectively prevents the risk of sample failure caused by enrichment membrane blockage, ensuring sampling efficiency and biological capture stability, while improving the intelligence of the unmanned surface vessel sampling system.

[0113] S400. After enriching the microbial samples at each site, perform molecular activation treatment induced by electric field / magnetic field or temperature control, and perform real-time quantitative PCR reaction on the treated samples to record the Ct value of each site.

[0114] After in-situ enrichment at each sampling site is completed, the enrichment membrane is removed and placed in a sealed lysis chamber. This chamber is pre-filled with lysis buffer (such as a mixture of Tris-HCl, proteinase K, and a nonionic surfactant) to assist in cell membrane rupture and nucleic acid release. The lysis chamber is sealed to the sample detection device to prevent aerosol contamination.

[0115] To improve the cell lysis efficiency of the target pathogen and maximize the release of DNA / RNA templates, it is preferable to use any one or a combination of the following three physical stimulation methods:

[0116] Symmetrically arranged electrode plates were placed at both ends of the lysis chamber, and an alternating square wave electric field of 5 to 10 volts was applied, with the frequency controlled between 500 Hz and 1000 Hz. The alternating electric field induced polarization and perforation of the microbial cell membrane, forming temporary electroporation, which promoted the leakage of nucleic acids. The treatment lasted for 90 to 120 seconds.

[0117] If a paramagnetic nanoparticle coating is added to the surface of the enrichment membrane, a uniform magnetic field of 0.2 Tesla to 0.5 Tesla can be applied using an external magnetic loading device to drive the particles to generate mechanical force that tears the microbial cell membrane, releasing the internal nucleic acid. The magnetic field loading time is controlled between 60 and 180 seconds.

[0118] An embedded heating element was used to rapidly raise the temperature of the lysis chamber to 65 degrees Celsius and maintain it for 5 minutes. This temperature activated the synergistic effect of proteases and nonionic surfactants in the lysis buffer, causing denaturation and rupture of the cell wall and membrane structures, releasing the target nucleic acid molecules.

[0119] The above three induction methods can be used individually or in combination in staggered or parallel modes, and the strategy can be adjusted according to the type of target pathogen and the complexity of the sample background.

[0120] After activation, the treated lysate was briefly precipitated by microcentrifugation, and 5 μL of the supernatant was used as a reaction template and added to a total reaction system of 20 μL. The system included target primers, probes, Taq enzyme, dNTP mixture, and buffer system. The reagent system was designed with sequence specificity according to the target organism (such as Shigella, Escherichia coli, rotavirus, etc.).

[0121] Inject the reaction system into the miniature real-time PCR reaction chamber and execute the standard three-step amplification procedure:

[0122] Transformation phase: 95 degrees Celsius, lasting 15 seconds;

[0123] Annealing stage: 60 degrees Celsius for 30 seconds;

[0124] Extension phase: 72 degrees Celsius, lasting 30 seconds;

[0125] The above process is repeated 40 times. During this process, the fluorescence signal acquisition device records the fluorescence intensity once at the end of each cycle. The cycle threshold (Ct value) of the sample is determined by the fluorescence threshold method, which is the number of cycles in which the fluorescence signal first exceeds the set threshold.

[0126] The smaller the Ct value, the higher the initial template concentration and the greater the content of the target pathogenic microorganism in the sample. The Ct value, corresponding coordinates, detection time, and activation method of each point are recorded by the system and uploaded to the data processing platform for subsequent mapping of water pathogen distribution and concentration level assessment.

[0127] Through the above steps, this invention introduces multimodal physical-induced activation technology into the detection of pathogens in water bodies, which effectively improves the efficiency of microbial lysis and the purity of nucleic acid extraction in samples, significantly enhances the sensitivity and stability of PCR detection, and is particularly suitable for high-specificity detection scenarios of low-concentration samples.

[0128] S500: Based on the Ct value distribution, determine whether there is a region with a strong positive signal. If so, automatically return to the region and perform multiple repeated sampling verifications.

[0129] In this invention, to enhance the ability to identify aquatic pathogen aggregation areas, spatial modeling and exponential analysis are performed on the cyclic threshold Ct values ​​of each sampling point, specifically including the following steps:

[0130] First, the cyclic threshold Ct values ​​of all sampling points after enrichment detection are mapped to a two-dimensional spatial grid according to their recorded geographic coordinates at the time of sampling. The entire target water area is divided into equally spaced regular grids, preferably with a grid side length of 50 meters. Each grid cell corresponds to one or more actual sampling points. For each grid center point, the Ct values ​​of the adjacent grids in eight directions are selected, and the differences between these values ​​and the Ct value of the center point are calculated. The variance of all differences is then calculated. This variance value is used to characterize the degree of abrupt change in the Ct value of that point in space and is defined as the spatial gradient variance exponent of Ct value, denoted as Gsv. The larger this exponent is, the more significant the difference between the Ct value of that point and the neighboring area, which may represent a local high concentration enrichment.

[0131] Set a positive threshold Tc for Ct values, preferably 30, that is, define all points with Ct values ​​less than or equal to 30 as suspected positive points. Calculate the cumulative Ct value offset density index among suspected positive points, the specific steps of which include:

[0132] First, the geographic coordinates of each actual sampling point are used as nodes in the graph structure. A neighborhood connection radius R1 (preferably 300 meters) is set, and pairwise Euclidean distances are calculated for all sampling points. If the straight-line distance between any two points is less than or equal to R1, an undirected edge is established between the corresponding two nodes, indicating that they have a spatial adjacency relationship. This process is repeated until all adjacency relationships between points are established, resulting in an undirected graph G=(V,E) containing a spatial topology, where V is the set of points and E is the set of edges. This graph structure is used to capture the spatial correlation between points, providing a basic connection framework for subsequent feature propagation.

[0133] For each node (i.e., sampling point) in the graph structure, an initial feature vector is constructed, containing the following two dimensions: Normalized Ct value feature: The original Ct value of each point is min-max normalized and mapped to the [0,1] interval; Local positive density feature: A positive Ct value judgment threshold Tc is set (e.g., 30), and the number n of the nodes' neighboring points with Ct values ​​less than Tc is counted. n is then divided by the total number of neighboring points to obtain the local positive density value of the node. The above two dimensions are combined to form the initial feature vector Xv of each node, which is then input into the graph neural network model.

[0134] Based on the constructed graph structure G and node feature matrix X, a multi-layer graph convolutional neural network is used for spatial feature propagation. The network structure includes:

[0135] Input layer: Receives raw node features;

[0136] At least two graph convolutional layers are required, each performing feature aggregation and nonlinear transformation once. The propagation formula is as follows: Where: Hi is the input feature of the i-th layer, Wi is the trainable weight matrix, σ is the activation function (such as ReLU), and  is the normalized form of the adjacency matrix. The output layer generates a high-dimensional aggregated feature representation Zv for each node, representing the spatial aggregated representation of that point in the graph structure after neighborhood propagation.

[0137] For each node, calculate its Ct value offset intensity Pv, defined as: Pv = Tc - original Ct value (Pv is positive when Ct value is less than Tc); perform a weighted inner product of the node's graph convolution output Zv and the offset intensity Pv to obtain the cumulative Ct value offset density Cdiv for that point: The "•" symbol represents the weighted product of a vector and a scalar. A higher exponent value indicates that the location is in an area with high pathogen concentration and strong aggregation.

[0138] Ultimately, the Cdiv values ​​of all nodes are mapped to their original geographic coordinates for the identification and spatial visualization of areas with strong positive signals.

[0139] The spatial distribution parameters Gsv and Cdi are input into a two-dimensional space for joint discrimination. A reference distribution curve is established using historical monitoring data, and thresholds Gthr for Gsv and Cthr for Cdi are defined, preferably by adding twice the standard deviation to their respective historical means.

[0140] If Gsv≥Gthr and Cdi≥Cthr for a certain grid cell, then the grid cell is determined to be a positive strong signal region; if any index does not meet the threshold condition, it is considered to be a normal fluctuation region.

[0141] After identifying a region with a strong positive signal, the system sends its geographic coordinates to the unmanned surface vessel (USV) path scheduling program, triggering feedback control.

[0142] The unmanned surface vessel automatically re-navigated to the center of the identified area;

[0143] In this area, a high-density sampling task is deployed with more sampling points than the original number of points. It is preferable to set up 4 to 6 new sampling points evenly distributed within a 200-meter radius.

[0144] The sampling and enrichment process was repeated, and the same PCR testing procedure was performed on the repeated samples to verify the accuracy of the first round of judgment.

[0145] If repeated sampling still meets the joint index threshold determination criteria, then the area is finally confirmed as a high-risk pathogen enrichment area, and an early warning report is generated.

[0146] S600. Based on the verification results, output the final spatial distribution map and concentration level map of pathogens in the target water body to guide on-site water quality risk assessment and emergency response deployment.

[0147] In this invention, to achieve continuous spatial distribution analysis and positive risk visualization of pathogenic microorganisms in target water bodies, a spatial prediction model of the cycle threshold (Ct value) is constructed using geostatistical methods, and multi-level pathogen concentration and probability maps are output. The specific steps are as follows:

[0148] First, the cyclic threshold Ct values ​​of all sampling points and their recorded geographic coordinates (latitude and longitude) are used as input data and imported into the spatial modeling process. Ordinary Kriging interpolation is employed as the modeling method to quantitatively model the variability of Ct values ​​in two-dimensional space. Specifically, this includes: calculating the squared difference of Ct values ​​between all sampling point pairs and grouping them according to spatial distance between the pairs; calculating the semivariogram values ​​for each distance group to obtain the empirical semivariogram function; selecting the fitting model type, including a spherical model, exponential model, or Gaussian model, and fitting the empirical semivariogram function using the least squares method; and determining the parameter set of the optimal model, including sill values, range values, and initial values, to describe the spatial correlation strength and decay trend of Ct values.

[0149] Using the established spatial variogram model, the entire target water area is divided into equally spaced grids (e.g., 50m x 50m per grid), and all grid points are used as interpolation target points. For each grid point, a standard kriging algorithm is used:

[0150] Calculate the spatial weight of this grid point relative to all sampling points;

[0151] Multiply the Ct value of the sampling point by the weight and sum them up by weight to obtain the predicted Ct value of that grid point;

[0152] The variance of the predicted value is also output as a basis for evaluating the prediction error.

[0153] Finally, a set of predicted Ct values ​​on a continuous spatial grid is obtained, which constitutes a continuous representation of the concentration field.

[0154] Set the positive decision threshold Tct for the cyclic threshold Ct, preferably 30. Calculate the predicted Ct values ​​μ and prediction variance σ for all grid points. 2 Assuming it follows a normal distribution, calculate the probability P that the Ct value of each grid point is less than Tct. This probability can be calculated by the standard normal distribution function Φ: P=Φ((Tct-μ) / σ); obtain the probability that the pathogen concentration at each location in the entire water body is lower than the judgment threshold, i.e., the positive probability distribution map, which is used to identify potentially high-risk areas.

[0155] The Ct value prediction results are fused with the positive probability results to output two types of spatial maps:

[0156] Ct value spatial distribution map: Based on the predicted Ct value, multiple concentration level intervals are divided, such as Ct≤25, 25<Ct≤30、Ct> Level 30, generate a graded color fill map;

[0157] Positive probability distribution map: Fill in the positive probability P of each grid point with color to identify high probability clustering areas (e.g., P≥0.8).

[0158] The images can be output as GeoTIFF or vector graphics, embedded in a geographic information system platform, and overlaid with hydrological and topographic maps for on-site risk assessment, deployment, and emergency response decision support.

[0159] Through the above steps, this invention combines geostatistical interpolation prediction with positive probability modeling, which not only achieves high-precision inference of Ct values ​​in unsampled areas, but also has the ability to predict the spatial spread of pathogen risks, significantly enhancing the accuracy and decision support value of water quality monitoring in practical applications.

[0160] Example 2, please refer to Figure 2 As shown in this embodiment, a water-based pathogen detection system includes:

[0161] High-risk identification module: acquires historical hydrological data and seasonal microbial distribution models of the target water body area, and combines them with meteorological forecast data to generate a set of coordinates P of high-risk areas of suspected pathogens at multiple locations in the water body;

[0162] Path planning module: Based on the coordinate set P of high-risk areas, control the unmanned surface vessel to autonomously cruise to each target point, and initiate water sampling and microbial enrichment at each point;

[0163] Dynamic judgment module: During the enrichment process, the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane are recorded at each point, and it is determined whether ΔP exceeds the preset threshold Pmax. If so, the current sampling is stopped and the process moves to the next point; otherwise, sampling continues until the set volume is reached.

[0164] Detection module: The enriched microbial samples at each site are subjected to molecular activation treatment induced by electric field / magnetic field or temperature control, and the treated samples are subjected to real-time quantitative PCR reaction, and the Ct value of each site is recorded.

[0165] Anomaly detection module: Based on the distribution of Ct values, determine whether there are areas with strong positive signals. If so, automatically return to the area and perform multiple repeated sampling verifications.

[0166] Visualization output module: Based on the verification results, outputs the final spatial distribution map and concentration level map of pathogens in the target water body, which can be used to guide on-site water quality risk assessment and emergency response deployment.

[0167] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for detecting pathogenic organisms in aquatic bodies, characterized in that: include: S100. Obtain historical hydrological data and seasonal microbial distribution models for the target water body area, and combine them with meteorological forecast data to generate a set of coordinates P of high-risk areas of suspected pathogens at multiple locations in the water body. S200: Based on the coordinate set P of the high-risk area, control the unmanned surface vessel to autonomously cruise to each target point, and initiate water sampling and microbial enrichment at each point; S300. During the enrichment process, record the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane at each point, and determine whether ΔP exceeds the preset threshold Pmax. If so, stop the current sampling and move to the next point; otherwise, continue sampling until the set volume is reached. S400. After enriching the microbial samples at each site, perform molecular activation treatment induced by electric field / magnetic field or temperature control, and perform real-time quantitative PCR reaction on the treated samples to record the Ct value of each site. S500: Based on the Ct value distribution, determine whether there is a region with a strong positive signal. If so, automatically return to the region and perform multiple repeated sampling verifications. S600. Based on the verification results, output the final spatial distribution map and concentration level map of pathogens in the target water body to guide on-site water quality risk assessment and emergency response deployment.

2. The method for detecting pathogenic organisms in water bodies according to claim 1, characterized in that: The steps for generating the set of coordinates P of high-risk areas for suspected pathogens at multiple locations in the water body include: Acquire historical hydrological data of the target water body over the past period, including water flow velocity, water temperature, rainfall and sunshine duration, and perform time-series normalization on the historical hydrological data; Based on historical hydrological data, a spatiotemporal distribution prediction model for pathogen concentration was constructed, and a support vector regression algorithm was used to establish the fitting relationship between various hydrological factors and pathogen concentration. By combining current meteorological forecast data and modeling results, the pathogen concentration risk value in each grid cell of the target water body is calculated, and a risk heat map is obtained. Grid cells in the risk heatmap where the pathogen concentration exceeds a set concentration threshold are extracted as high-risk areas, and corresponding geographic coordinate points are generated to form a high-risk area coordinate set P.

3. The method for detecting pathogenic organisms in water bodies according to claim 1, characterized in that: The steps for controlling an unmanned surface vessel to autonomously navigate to various target locations based on the coordinate set P of high-risk areas include: The coordinate points in the high-risk area coordinate set P are sorted according to the shortest navigation distance principle to generate a continuous cruise path, and the sorting result is converted into a geographic coordinate navigation command sequence that can be recognized by the unmanned surface vessel. After receiving the navigation command sequence, the unmanned surface vessel (USV) uses satellite positioning data and heading sensor data to calculate in real time the offset distance and heading deviation between the current position of the USV and the target point. When the distance between the unmanned surface vessel and the target location is less than the preset arrival judgment radius, it automatically reduces its propulsion speed and enters a fixed-point navigation state. After completing the stay at the current location, mark the current location as a completed sampling point.

4. The method for detecting pathogenic organisms in water bodies according to claim 1, characterized in that: The step of determining whether the pressure difference ΔP across the enrichment membrane exceeds the preset threshold Pmax includes: A first pressure sensor and a second pressure sensor are respectively installed at the inlet and outlet of the enrichment membrane to obtain the inlet water pressure P1 and the outlet water pressure P2 in real time. Simultaneous sampling of P1 and P2 was performed at fixed time intervals, and the pressure difference across the enrichment membrane was calculated. ; Will Compare with the set differential pressure threshold Pmax, if If the value is greater than or equal to Pmax, it is determined that the enrichment membrane is showing signs of clogging, and the sampling device is controlled to stop pumping water and generate an alarm signal.

5. The method for detecting pathogenic organisms in water bodies according to claim 1, characterized in that: Based on the Ct value distribution, determine whether there are regions with strong positive signals, including: The cyclic threshold Ct values ​​of all sampling points are mapped to a two-dimensional spatial grid according to geographic coordinates, and the spatial gradient variance exponent of Ct values ​​between each point and its adjacent points is calculated. Set a positive threshold for Ct value, extract all points with Ct values ​​less than the positive threshold as suspected positive points, and generate a cumulative Ct value offset density index in their local neighborhood. The spatial gradient variance index of Ct values ​​and the cumulative index of Ct value offset density are jointly analyzed; If the spatial gradient variance index of Ct value and the cumulative index of Ct value offset density both exceed the preset threshold in a certain region, the region is determined to be a positive strong signal region.

6. The method for detecting pathogenic organisms in water bodies according to claim 5, characterized in that: The steps for generating the Ct value offset density accumulation index include: The geographic coordinates of each sampling point are used as nodes in the graph structure, and adjacency relationships are constructed based on the spatial distance between the points. When the distance between points is less than the set neighborhood radius, a connecting edge is established, thereby forming a spatial association graph structure. The cyclic threshold Ct value and the density of positive points in the local neighborhood of each node are used as node features input to the graph neural network. The spatial clustering intensity value of each node is calculated based on the node feature vector after propagation, and then weighted and accumulated in combination with the original Ct value offset degree to obtain the Ct value offset density cumulative index of each sampling point.

7. The method for detecting pathogenic organisms in water bodies according to claim 1, characterized in that: The step of outputting the final spatial distribution map and concentration level map of the pathogen in the target water body based on the verification results includes: Input the cyclic threshold Ct values ​​and their corresponding coordinates of all sampling points into the spatial modeling module, construct the spatial variogram model of Ct values ​​using the ordinary Kriging interpolation algorithm, and determine the optimal fitting semi-variogram type and parameter set. Based on the modeling results, the predicted Ct values ​​and prediction errors of all unsampled grid points in the target water body region are calculated to obtain a complete spatial continuous concentration distribution dataset. The predicted Ct value is compared with the set positive threshold, the probability that the Ct value of each grid point is less than the positive threshold is calculated, and the positive probability distribution map is output. Based on the predicted Ct values ​​and positive probability results, a spatial distribution map and multi-level concentration map of pathogens in the target water body are generated.

8. A system for detecting pathogenic organisms in aquatic bodies, used to implement the method for detecting pathogenic organisms in aquatic bodies as described in any one of claims 1-7, characterized in that: include: High-risk identification module: acquires historical hydrological data and seasonal microbial distribution models of the target water body area, and combines them with meteorological forecast data to generate a set of coordinates P of high-risk areas of suspected pathogens at multiple locations in the water body; Path planning module: Based on the coordinate set P of high-risk areas, control the unmanned surface vessel to autonomously cruise to each target point, and initiate water sampling and microbial enrichment at each point; Dynamic judgment module: During the enrichment process, the real-time water temperature, pH, turbidity and pressure difference ΔP across the enrichment membrane are recorded at each point, and it is determined whether ΔP exceeds the preset threshold Pmax. If so, the current sampling is stopped and the process moves to the next point; otherwise, sampling continues until the set volume is reached. Detection module: The enriched microbial samples at each site are subjected to molecular activation treatment induced by electric field / magnetic field or temperature control, and the treated samples are subjected to real-time quantitative PCR reaction, and the Ct value of each site is recorded. Anomaly detection module: Based on the distribution of Ct values, determine whether there are areas with strong positive signals. If so, automatically return to the area and perform multiple repeated sampling verifications. Visualization output module: Based on the verification results, outputs the final spatial distribution map and concentration level map of pathogens in the target water body, which can be used to guide on-site water quality risk assessment and emergency response deployment.

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