A tap water real-time purification control method and device based on edge computing and a medium

Abstract

The application discloses a kind of based on edge computing's tap water real-time purification control method, equipment and medium, it is related to automatic control technical field, including, obtain offset fluorescence wavelength and attenuation intensity characteristic, generate optical characteristic data stream;In fixed time window, the attenuation gradient and displacement vector of optical characteristic data stream are extracted, and attenuation gradient and displacement vector are convolved in space and construct three-dimensional pollutant atlas;According to the vector distribution mode diagram of pollutant distribution in three-dimensional pollutant atlas, output magnetic field control parameter;Magnetic field control parameter is input to magnetic field generator, and generates branched catalytic cluster;Through branched catalytic cluster, catalytic degradation is carried out to unpurified water source, and generates catalytic process curve;The coincidence degree of comparison catalytic process curve and preset target curve is completed tap water real-time purification control.The application realizes the adaptive real-time purification of heavy metal pollution by the synergy of three-dimensional pollutant atlas construction and catalytic process closed-loop control.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and in particular to a method, equipment and medium for real-time purification control of tap water based on edge computing. Background Technology

[0002] In recent years, the industrial water treatment field has accelerated its evolution towards edge intelligence and precise physical field control. Edge computing-based tap water purification technology, by deploying optical sensing and real-time analysis nodes at water facilities, has significantly shortened the decision-making delay from pollutant identification to treatment. Among these technologies, quantum dot fluorescence spectroscopy combined with turbulent field modeling has become the mainstream solution for heavy metal pollution detection. Existing technologies have achieved breakthroughs in reducing sludge pollution by using magnetically controlled nanoparticles for non-chemical purification. The spatiotemporal fusion of spectral attenuation gradients and displacement vectors by edge nodes provides a data foundation for constructing three-dimensional distribution maps of pollutants. The development of multi-physics collaborative control theory has promoted the integrated process from pollution identification to purification execution.

[0003] However, existing technologies have shortcomings in terms of real-time response, distributed adaptability, and closed-loop control. Traditional cloud-based spectral analysis suffers from latency, making it difficult to meet the real-time requirements for handling sudden heavy metal pollution. Static magnetic field control strategies cannot adapt to the spatial gradient distribution characteristics of pollutants, resulting in insufficient catalytic efficiency of nanoparticles. Monitoring of the purification process relies on offline water quality testing. The lack of closed-loop feedback between electrochemical sensor data and purification execution equipment leads to lag in the catalytic process or even over-purification. Edge computing resource allocation does not incorporate fluid dynamics characteristics, resulting in decreased control accuracy in high-concentration pollution areas. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a real-time tap water purification control method based on edge computing to solve the problems of insufficient response time, distribution adaptability and control closed loop.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a real-time tap water purification control method based on edge computing, comprising: acquiring offset fluorescence wavelength and attenuation intensity characteristics to generate an optical feature data stream; extracting the attenuation gradient and displacement vector of the optical feature data stream within a fixed time window, spatially convolving the attenuation gradient and displacement vector to construct a three-dimensional pollutant map; extracting a vector distribution pattern map of pollutants in the three-dimensional pollutant map, setting the magnetic field action area and magnetic field strength according to the vector distribution pattern map, and outputting magnetic field control parameters; inputting the magnetic field control parameters to a magnetic field generator, which generates a rotating magnetic field by matching the magnetic field strength to the magnetic field control parameters, and generating a branched catalytic cluster by performing vortex motion in the rotating magnetic field; catalytically degrading unpurified water source through the branched catalytic cluster, while simultaneously monitoring the spectral absorbance and potential fluctuation of the catalytic degradation area in real time through an electrochemical sensor to generate a catalytic process curve; comparing the degree of agreement between the catalytic process curve and the preset target curve to determine whether the magnetic field control parameters need to be adjusted, thereby completing the real-time purification control of tap water.

[0008] As a preferred embodiment of the edge computing-based real-time tap water purification and control method of the present invention, the specific steps of acquiring the offset fluorescence wavelength and attenuation intensity characteristics and generating an optical feature data stream are as follows:

[0009] Integrating quantum dot films into the pipeline monitoring section of water treatment pipelines;

[0010] A laser beam is emitted to irradiate a quantum dot film, exciting the quantum dot film to generate a fluorescence signal;

[0011] Feature extraction is performed on the fluorescence signal to obtain the offset fluorescence wavelength and attenuation intensity characteristics;

[0012] The offset fluorescence wavelength and attenuation intensity characteristics are compared with the standard fluorescence spectrum to output an optical characteristic data stream.

[0013] As a preferred embodiment of the edge computing-based real-time tap water purification and control method of the present invention, the specific steps of extracting the attenuation gradient and displacement vector of the optical feature data stream within a fixed time window, performing spatial convolution on the attenuation gradient and displacement vector, and constructing a three-dimensional pollutant map are as follows:

[0014] Within a fixed time window, the absolute value of the difference between the fluorescence intensity attenuation values ​​at adjacent time points in the optical feature data stream is extracted to generate an attenuation gradient sequence;

[0015] The difference between the offset fluorescence wavelengths of adjacent spatial points in the optical feature data stream is extracted to generate a displacement vector sequence.

[0016] Map the decay gradient sequence and the displacement vector sequence to decay gradient matrix and displacement vector matrix, respectively;

[0017] A sliding window algorithm is used to perform pointwise convolution on the decay gradient matrix and the displacement vector matrix to generate a spatial correlation matrix.

[0018] The spatial correlation matrix is ​​overlaid with the timestamps and spatial coordinates in the optical feature data stream to form three-dimensional environmental data.

[0019] Visualization tools are used to perform spatiotemporal intensity visualization mapping of 3D environmental data, outputting a 3D pollutant map.

[0020] As a preferred embodiment of the edge computing-based real-time tap water purification and control method of the present invention, the steps of extracting a vector distribution pattern map of pollutants from a three-dimensional pollutant map, setting the magnetic field action area and magnetic field strength according to the vector distribution pattern map, and outputting magnetic field control parameters are as follows:

[0021] Pollutant distribution information of displacement vector sequence is extracted from three-dimensional pollutant map, and spatial correlation of pollutant distribution information is performed to generate vector distribution pattern map;

[0022] Based on the pollutant migration direction and pollutant distribution area in the vector distribution pattern diagram, the pipeline monitoring section is divided into multiple magnetic field action areas, and the boundary coordinates of the magnetic field action areas are recorded.

[0023] Set the magnetic field strength value according to the vector amplitude of each magnetic field's area of ​​action;

[0024] The boundary coordinates, magnetic field strength values, and pollutant migration directions of each magnetic field's active region are combined to form magnetic field control parameters.

[0025] As a preferred embodiment of the edge computing-based real-time tap water purification and control method of the present invention, the step of inputting magnetic field control parameters to a magnetic field generator, and the magnetic field generator generating a rotating magnetic field by matching the corresponding magnetic field strength according to the magnetic field control parameters, specifically includes the following steps:

[0026] Input the magnetic field control parameters into the magnetic field generator;

[0027] The current input intensity and energizing sequence of the electromagnetic coil array in the magnetic field generator are adjusted by controlling the magnetic field parameters.

[0028] After completing the current input intensity adjustment and energizing timing settings, start the electromagnetic coil array and activate the electromagnetic coils in the array according to the energizing timing to generate a rotating magnetic field.

[0029] As a preferred embodiment of the edge computing-based real-time water purification and control method of the present invention, the vortex motion in the rotating magnetic field refers to the pre-placed nanoparticles being driven by the periodic change in the direction of the rotating magnetic field under the action of the rotating magnetic field, and moving in a spiral trajectory along the rotation axis of the rotating magnetic field.

[0030] As a preferred embodiment of the edge computing-based real-time tap water purification and control method of the present invention, the method involves: catalytically degrading unpurified water sources through branched catalytic clusters, while simultaneously monitoring the spectral absorbance and potential fluctuations of the catalytic degradation region in real time using electrochemical sensors to generate a catalytic process curve. The specific steps are as follows:

[0031] Unpurified water sources are catalytically degraded using branched catalytic clusters, and the catalytic degradation zone is output.

[0032] An electrochemical sensor monitors the spectral absorbance and potential fluctuations in the catalytic degradation region in real time and outputs spectral absorbance and potential fluctuation signals.

[0033] The spectral absorbance signal and the potential fluctuation signal are time-synchronized fusion processed to generate a time-intensity correlated signal sequence;

[0034] The time-intensity correlated signal sequence is normalized, and the catalytic degradation efficiency is mapped onto the normalized time-intensity correlated signal sequence to generate a catalytic process curve.

[0035] As a preferred embodiment of the edge computing-based real-time tap water purification control method of the present invention, the specific steps for comparing the degree of agreement between the catalytic process curve and the preset target curve to determine whether the magnetic field control parameters need to be adjusted to complete the real-time tap water purification control are as follows:

[0036] Based on the water purification requirements, a target curve is set, and the catalytic process curve and the target curve are compared point by point on the same time axis.

[0037] If the shape of the catalytic process curve is inconsistent with that of the target curve, the magnetic field control parameters will be adjusted to correct the catalytic process curve.

[0038] If the catalytic process curve matches the target curve, then continue to maintain real-time purification of tap water until the tap water purification control is completed.

[0039] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the edge computing-based real-time water purification control method as described in the first aspect of the present invention.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the edge computing-based real-time water purification control method as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: Adaptive real-time purification of heavy metal pollution is achieved through the synergy of three-dimensional pollutant mapping and closed-loop control of the catalytic process. Within a fixed time window, the attenuation gradient and displacement vector of the optical feature data stream are extracted and spatially convolutionally processed to generate a three-dimensional pollutant map. This accurately quantifies the spatial distribution gradient and migration direction of pollutants, providing a dynamic modeling basis for the division of the magnetic field's active area. Based on the magnetic field control parameters generated from the map, a rotating magnetic field is driven to form a branched catalytic cluster. The multi-level branching structure significantly increases the density of active sites. Furthermore, an electrochemical sensor synchronously monitors the catalytic degradation region to generate a catalytic process curve. The magnetic field parameters are dynamically adjusted based on the target curve's fit, forming a millisecond-level closed-loop control from pollutant localization and cluster structure optimization to degradation feedback, ensuring the consistency between purification efficiency and water quality safety. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a real-time water purification and control method based on edge computing.

[0044] Figure 2 A flowchart for outputting optical feature data stream;

[0045] Figure 3 A flowchart for constructing a three-dimensional pollutant map;

[0046] Figure 4 The flowchart for executing the magnetic field control parameters. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 This is one embodiment of the present invention, which provides a real-time tap water purification and control method based on edge computing, comprising the following steps:

[0051] S1. Obtain the offset fluorescence wavelength and attenuation intensity characteristics to generate an optical feature data stream.

[0052] Integrating quantum dot films into the pipeline monitoring section of water treatment pipelines;

[0053] Specifically, in the process of tap water purification, in order to achieve real-time monitoring of water quality, a quantum dot film is first integrated into the monitoring section of the water treatment pipeline. The monitoring section refers to a fixed part of the water treatment pipeline specifically used for water quality detection, usually located in an area where the water flow is stable and it is easy to install sensing equipment. The quantum dot film is a thin-layer structure made of nanoscale semiconductor materials with unique fluorescence properties. It can emit fluorescence signals under light excitation at a specific wavelength and is used to detect pollutants in the water. The integration process includes the following operations: First, select a quantum dot film to ensure its chemical stability and water resistance to adapt to the harsh environment of long-term exposure to water flow inside the pipeline. Then, uniformly attach the quantum dot film to the inner wall of the monitoring section of the pipeline or a special transparent substrate to ensure that the film is in full contact with the water flow without affecting the water flow dynamics. Next, calibrate the installation position of the quantum dot film to ensure that the surface of the quantum dot film can be accurately irradiated by the subsequent laser beam, while avoiding interference from turbulence or impurity deposition in the water flow inside the pipeline with the generation of fluorescence signals.

[0054] A laser beam is emitted to irradiate a quantum dot film, exciting the quantum dot film to generate a fluorescence signal;

[0055] After integrating quantum dot films into the pipeline monitoring section of a water treatment pipeline, a laser beam is emitted from a laser to irradiate the quantum dot film, thereby exciting it to generate a fluorescence signal. A specific wavelength matching the absorption spectrum of the quantum dot film is selected. After the laser is activated, it continuously or pulses to emit a laser beam. The laser beam passes through the transparent window of the pipeline monitoring section or directly penetrates the pipe wall to irradiate the surface of the quantum dot film. When the laser beam interacts with the nanoparticles in the quantum dot film, the quantum dot film absorbs photon energy, and electrons transition from the ground state to the excited state. Subsequently, when returning to the ground state, it releases a fluorescence signal. The wavelength and intensity of the fluorescence signal are affected by pollutants in the water, such as organic matter, microorganisms, or other chemicals, which may cause a shift in fluorescence wavelength or an attenuation in intensity. The generation process of the fluorescence signal takes place in real time within the pipeline monitoring section, and the signal intensity and wavelength directly reflect the dynamic changes in water quality.

[0056] Feature extraction is performed on the fluorescence signal to obtain the offset fluorescence wavelength and attenuation intensity characteristics;

[0057] Fluorescence signals are captured by optical sensors installed in the pipeline monitoring section. The sensors include a high-resolution spectrometer, which can accurately measure the wavelength and intensity of the fluorescence signal. The fluorescence signals captured by the optical sensors are decomposed into spectral components by high-precision spectral analysis equipment. The offset fluorescence wavelength and attenuation intensity characteristics at each time point are recorded. The offset fluorescence wavelength and attenuation intensity characteristics represent the unpurified wavelength of the unpurified water source, i.e., the offset fluorescence wavelength and intensity attenuation value. The offset fluorescence wavelength and attenuation intensity characteristics are integrated to form a time series dataset.

[0058] The offset fluorescence wavelength and attenuation intensity characteristics are compared with the standard fluorescence spectrum to output an optical characteristic data stream;

[0059] Standard fluorescence spectroscopy refers to the original fluorescence wavelength and fluorescence intensity characteristics obtained from purified water sources using quantum dot films. The original fluorescence wavelength and intensity characteristics are stored as a benchmark dataset, containing the original wavelength and fluorescence intensity values ​​of the quantum dot films in the purified water source. The intensity attenuation values ​​of the offset fluorescence wavelength and attenuation intensity characteristics recorded in the time-series dataset are matched one-to-one with the original wavelength and fluorescence intensity values ​​of the standard fluorescence spectrum. The fluorescence wavelength offset and fluorescence intensity attenuation value are calculated. The fluorescence wavelength offset is obtained by comparing the peak value of the offset fluorescence wavelength at each time point in the time-series dataset with the peak value of the original wavelength. The fluorescence wavelength offset represents the wavelength change caused by pollutants in the water body; for example, organic matter or microorganisms may cause the wavelength to shift towards the red or blue end. The fluorescence intensity attenuation value is obtained by comparing the intensity attenuation value at each time point in the time-series dataset with the peak value of the fluorescence intensity value. The fluorescence intensity attenuation value reflects the absorption or scattering effect of pollutants on the fluorescence signal.

[0060] The formula for fluorescence wavelength shift is:

[0061] ;

[0062] The formula for fluorescence intensity attenuation is:

[0063] ;

[0064] in, This indicates the amount of fluorescence wavelength offset from the original wavelength. This indicates the offset fluorescence wavelength of the unpurified water source. This represents the original wavelength used to purify the water source. The fluorescence intensity attenuation value represents the difference between the intensity attenuation value and the fluorescence intensity value. The fluorescence intensity value of the purified water source is displayed. This indicates the intensity attenuation value of unpurified water sources;

[0065] The calculated fluorescence wavelength offset and fluorescence intensity attenuation values ​​are organized according to time series to form a feature dataset. The feature dataset is then added with timestamps and spatial coordinates corresponding to the pipeline monitoring section through an edge computing device. The timestamps record the acquisition time of each feature data, and the spatial coordinates reflect the physical location of the quantum dot film within the pipeline monitoring section, forming a structured optical feature data stream.

[0066] S2. Extract the attenuation gradient and displacement vector of the optical feature data stream within a fixed time window, perform spatial convolution on the attenuation gradient and displacement vector, and construct a three-dimensional pollutant map.

[0067] Within a fixed time window, the absolute value of the difference between the fluorescence intensity attenuation values ​​at adjacent time points in the optical feature data stream is extracted to generate an attenuation gradient sequence;

[0068] Specifically, after acquiring the optical feature data stream, in order to extract the distribution trend of pollutants in the time dimension, the optical feature data stream is segmented using a fixed time window. The fixed time window refers to the continuous optical feature data stream being truncated in units of fixed duration. Within each fixed time window, the absolute value of the difference between the fluorescence intensity attenuation values ​​at adjacent time points is extracted to reflect the rate of intensity change per unit time. The extraction process of the absolute value of the difference is completed point by point by edge computing devices, and finally, an attenuation gradient sequence is generated. The attenuation gradient sequence reflects the dynamic distribution characteristics of pollutants in the time dimension.

[0069] The difference between the offset fluorescence wavelengths of adjacent spatial points in the optical feature data stream is extracted to generate a displacement vector sequence.

[0070] After the fixed time window processing is completed, the offset fluorescence wavelength of adjacent spatial points in the optical feature data stream is extracted. Adjacent spatial points refer to multiple quantum dot film sampling positions arranged along the water flow direction inside the pipeline monitoring section. The offset fluorescence wavelength varies in space due to different pollutant types and concentrations. By comparing the difference in offset fluorescence wavelength between adjacent spatial points, the changing trend of pollutant distribution in space is reflected. The generated displacement vector sequence contains the migration direction and distribution density information of pollutants in the spatial dimension, which together with the attenuation gradient sequence constitute the spatiotemporal feature basis of pollutant distribution.

[0071] Map the decay gradient sequence and the displacement vector sequence to decay gradient matrix and displacement vector matrix, respectively;

[0072] Each data point in the decay gradient sequence is arranged sequentially according to time and corresponding spatial position. The one-dimensional sequence is expanded into a time-space two-dimensional structure to form a decay gradient matrix in which rows represent time points and columns represent spatial positions. Each element in the decay gradient matrix corresponds to the fluorescence intensity change rate at a certain time point and a certain spatial position. Each data point in the displacement vector sequence is arranged sequentially according to the corresponding time point and spatial position in a row and column layout to form a displacement vector matrix in which rows represent the time dimension and columns represent the spatial dimension. The displacement vector matrix also has spatial position as columns and time as rows. Each element represents the difference in fluorescence wavelength between adjacent spatial points, which is used to characterize the spatial migration trend of pollutants.

[0073] A sliding window algorithm is used to perform pointwise convolution on the decay gradient matrix and the displacement vector matrix to generate a spatial correlation matrix.

[0074] Specifically, after the attenuation gradient matrix and displacement vector matrix are constructed, the sliding window algorithm is used to perform pointwise convolution on the two matrices. The sliding window algorithm refers to sliding a fixed-size window on the matrix and locally weighting the data area covered by the window to capture the spatial correlation of pollutants in the local area. The attenuation gradient matrix and displacement vector matrix are locally weighted by the sliding window algorithm, and the results of the local weighting of the attenuation gradient matrix and displacement vector matrix are fused (the results of the local weighting of the attenuation gradient matrix and displacement vector matrix are superimposed point by point according to the time-space correspondence position) to generate a spatial correlation matrix. The spatial correlation matrix reflects the comprehensive distribution characteristics of pollutants in the time and space dimensions. The comprehensive distribution characteristics are divided into: high value areas indicate areas with high or drastic changes in pollutant concentration, and low value areas indicate relatively uniform or stable pollutant distribution.

[0075] The spatial correlation matrix is ​​overlaid with the timestamps and spatial coordinates in the optical feature data stream to form three-dimensional environmental data.

[0076] The spatial correlation matrix is ​​overlaid with the corresponding timestamps and spatial coordinates in the optical feature data stream. The timestamps record the acquisition time of each data point in the optical feature data stream, while the spatial coordinates indicate the specific location of the data point in the pipeline monitoring section. By combining the acquisition time, specific location, and spatial correlation matrix, three-dimensional environmental data with time, space, and pollutant intensity attributes are formed. Each layer of the three-dimensional environmental data corresponds to a specific time point, and the pollutant distribution intensity is arranged according to spatial coordinates within each layer, providing a complete spatiotemporal data foundation for subsequent visualization.

[0077] The spatiotemporal intensity visualization mapping of 3D environmental data is performed using visualization tools to output a 3D pollutant map.

[0078] After the 3D environmental data is constructed, visualization tools (such as 3D graphics rendering software) are used to perform spatiotemporal intensity visualization mapping on the 3D environmental data. The visualization tools use color gradients, isosurfaces, or dynamic animations to intuitively present the distribution intensity of pollutants in the 3D environmental data in 3D space. Color gradients are used to represent the level of pollutant concentration, isosurfaces are used to mark the boundaries of pollutant distribution, and dynamic animations are used to show the changing trend of pollutants over time. Finally, a 3D pollutant map is output, which can clearly show the distribution area, migration path, and concentration change trend of pollutants in water treatment pipelines.

[0079] S3. Extract the vector distribution pattern map of pollutants from the three-dimensional pollutant map, set the magnetic field area and magnetic field strength according to the vector distribution pattern map, and output the magnetic field control parameters.

[0080] Pollutant distribution information of displacement vector sequence is extracted from three-dimensional pollutant map, and spatial correlation of pollutant distribution information is performed to generate vector distribution pattern map;

[0081] Specifically, the pollutant distribution information, derived from displacement vector sequences, is extracted from a 3D pollutant map. This involves identifying the trend of shifted fluorescence wavelengths along the water flow direction in the 3D pollutant map. This shifted fluorescence wavelength trend, represented by displacement vector sequences, indicates the spatial migration direction and distribution density of pollutants. Subsequently, spatial correlation is performed on the pollutant distribution information, including migration direction and distribution density. Spatial correlation refers to identifying the main migration paths and concentrated areas of pollutants by examining the continuity and directional consistency of pollutant distribution between adjacent spatial points. These main paths and concentrated areas together form a vector distribution pattern diagram. The vector distribution pattern diagram uses vector arrows to represent the pollutant migration direction and color depth to reflect pollutant distribution density, providing a basis for subsequent delineation of magnetic field influence areas.

[0082] Based on the pollutant migration direction and pollutant distribution area in the vector distribution pattern diagram, the pipeline monitoring section is divided into multiple magnetic field action areas, and the boundary coordinates of the magnetic field action areas are recorded.

[0083] Using the pollutant migration direction and distribution area information in the vector distribution pattern map, the monitoring section of the water treatment pipeline is divided into magnetic field areas. Based on the continuity of the pollutant concentration area and migration path, the monitoring section is divided into several spatial regions with clear boundaries. Each magnetic field area corresponds to a pollutant distribution area, and the pollutant distribution area has a specific pollutant migration direction. During the division process, the region with consistent vector direction and stable pollutant concentration in the vector distribution pattern map is identified as the magnetic field area by the edge computing device. After the division is completed, the boundary coordinates of each magnetic field area are recorded. The boundary coordinates refer to the start and end positions of the magnetic field area along the water flow direction in the monitoring section, which are used for the generation of subsequent magnetic field control parameters.

[0084] Set the magnetic field strength value according to the vector amplitude of each magnetic field's area of ​​action;

[0085] Vector amplitude represents the combined characteristics of pollutant migration speed and distribution density in the area affected by the magnetic field. The larger the vector amplitude, the denser the pollutant distribution and the more active the migration in the area affected by the magnetic field. The specific way to set the magnetic field strength value is based on the magnitude of the vector amplitude. The larger the vector amplitude, the larger the magnetic field strength value.

[0086] The boundary coordinates, magnetic field strength values, and pollutant migration directions of each magnetic field region are combined to form magnetic field control parameters.

[0087] The boundary coordinates of each magnetic field region, the corresponding magnetic field strength value, and the pollutant migration direction are combined to form structured magnetic field control parameters. The boundary coordinates are used to determine the spatial location of the magnetic field region, the magnetic field strength value is used to control the magnitude of the magnetic field within the region, and the pollutant migration direction is used to guide the setting of the magnetic field direction, ensuring that the magnetic field can work in synergy with the pollutant migration direction.

[0088] S4. Input the magnetic field control parameters to the magnetic field generator. The magnetic field generator matches the corresponding magnetic field strength according to the magnetic field control parameters to generate a rotating magnetic field. The rotating magnetic field produces vortex motion and generates branched catalytic clusters.

[0089] Input the magnetic field control parameters into the magnetic field generator;

[0090] The current input intensity and energizing sequence of the electromagnetic coil array in the magnetic field generator are adjusted by controlling the magnetic field parameters.

[0091] Specifically, each electromagnetic coil in the electromagnetic coil array is adjusted according to the magnetic field control parameters. For each magnetic field action area, the corresponding current intensity is set according to the magnitude of the magnetic field strength value so that the generated magnetic field strength meets the requirements. At the same time, based on the direction and speed of pollutant migration, the energizing sequence of the electromagnetic coils is arranged, i.e., the energizing timing, to form a rotating magnetic field environment that can drive nanoparticles along the direction of pollutant migration.

[0092] After completing the current input intensity adjustment and energizing timing settings, start the electromagnetic coil array and activate the electromagnetic coils in the electromagnetic coil array according to the energizing timing to generate a rotating magnetic field.

[0093] Vortex motion in a rotating magnetic field refers to the process where pre-placed nanoparticles, driven by the periodic changes in the direction of the rotating magnetic field, move in a spiral trajectory along the rotation axis of the rotating magnetic field, generating branched catalytic clusters.

[0094] Vortex motion in a rotating magnetic field refers to the process where nanoparticles pre-placed in an unpurified water source, driven by the periodic changes in the direction of the rotating magnetic field, move in a spiral trajectory along the axis of rotation of the rotating magnetic field, forming branched catalytic clusters. Once the rotating magnetic field is established, the nanoparticles dispersed in the water treatment pipeline begin to respond to the changes in the magnetic field. Due to the continuous changes in the direction and speed of the rotating magnetic field, the nanoparticles are affected by these changes and are forced to perform spiral vortex motion along the axis of rotation of the rotating magnetic field. During the vortex motion, the nanoparticles continuously collide and aggregate, gradually forming catalytic clusters with complex branched structures, i.e., branched catalytic clusters.

[0095] S5. Unpurified water source is catalytically degraded through branched catalytic clusters, and the spectral absorbance and potential fluctuations of the catalytic degradation area are monitored in real time by an electrochemical sensor to generate a catalytic process curve.

[0096] Unpurified water sources are catalytically degraded using branched catalytic clusters, and the catalytic degradation zone is output.

[0097] Specifically, the branched catalytic clusters have abundant active sites, which can efficiently adsorb organic pollutants in unpurified water sources and degrade the pollutants in the unpurified water sources into harmless substances through catalytic oxidation and reduction reactions. For example, when water contains trace amounts of phenol, the branched catalytic clusters can significantly accelerate the oxidation reaction between phenol and hydrogen peroxide, converting phenol into non-toxic quinone compounds and water, thereby achieving efficient purification of the water source. The process does not require external mechanical stirring. It relies on a rotating magnetic field to drive nanoparticles to self-organize and form a catalytic structure, improving catalytic efficiency and reaction rate, while reducing energy consumption and equipment complexity. It is suitable for real-time purification and control of tap water.

[0098] The catalytic degradation process occurs inside the monitoring section of the water treatment pipeline. As the branched catalytic clusters continue to move in the water flow, the surface of the branched catalytic clusters continuously reacts with pollutants to form multiple locally active catalytic degradation regions. The catalytic degradation region refers to the specific spatial range in which the branched catalytic clusters react chemically with the pollutants. The range and reaction intensity of the catalytic degradation region change dynamically over time.

[0099] An electrochemical sensor monitors the spectral absorbance and potential fluctuations in the catalytic degradation region in real time and outputs spectral absorbance and potential fluctuation signals.

[0100] The electrochemical sensor begins to monitor the catalytic degradation region in real time, monitoring two key parameters: spectral absorbance and potential fluctuation. Spectral absorbance reflects the change in pollutant concentration in the water. As catalytic degradation proceeds, pollutants are gradually decomposed, and spectral absorbance shows a decreasing trend. Potential fluctuation is used to characterize the activity of electron transfer during the catalytic reaction, indirectly reflecting the catalytic efficiency of the reaction. The electrochemical sensor collects spectral absorbance signals and potential fluctuation signals respectively through built-in optical detection devices and electrode arrays.

[0101] The spectral absorbance signal and the potential fluctuation signal are time-synchronized fusion processed to generate a time-intensity correlated signal sequence;

[0102] After acquiring the spectral absorbance signal and the potential fluctuation signal, time-synchronized fusion processing is performed on the spectral absorbance signal and the potential fluctuation signal to eliminate possible time deviations during signal acquisition. Time-synchronized fusion refers to aligning the two signals according to a unified time reference and establishing a one-to-one correspondence in the time dimension, ensuring that the spectral absorbance signal and the potential fluctuation signal at each time point correspond one-to-one. The time-synchronized fused spectral absorbance signal and potential fluctuation signal are organized in the form of time-intensity pairs, forming a time-intensity correlated signal sequence. Each data point in the time-intensity correlated signal sequence contains the spectral absorbance and potential fluctuation at the same time point, which can comprehensively reflect the dynamic changes of the catalytic degradation process.

[0103] The time-intensity correlated signal sequence is normalized, and the catalytic degradation efficiency is mapped onto the normalized time-intensity correlated signal sequence to generate a catalytic process curve;

[0104] Specifically, to improve the comparability and analytical accuracy of spectral absorbance and potential fluctuations in time-intensity correlated signal sequences, the time-intensity correlated signal sequences are normalized. Normalization refers to mapping spectral absorbance and potential fluctuations to a unified numerical range, typically between 0 and 1, to eliminate the influence of differences in the dimensions of different sensors. After normalization, the processed time-intensity correlated signal sequences are normalized according to catalytic mapping rules. Catalytic mapping rules refer to establishing a conversion relationship between normalized values ​​of spectral absorbance and potential fluctuations and catalytic degradation efficiency by combining the correspondence between the decrease in spectral absorbance and the change in potential fluctuations during catalytic degradation and the actual change trend of pollutant concentration per unit time. Specifically, at multiple time points, the normalized spectral absorbance values ​​and potential fluctuation values ​​are weighted and calculated to obtain a comprehensive reaction activity value. The higher the comprehensive reaction activity value, the higher the catalytic degradation efficiency per unit time. Finally, the comprehensive reaction activity values ​​at each time point are arranged in chronological order to form a catalytic progress curve for evaluating the overall trend of the catalytic process.

[0105] The formula for the comprehensive reactivity value is:

[0106] ;

[0107] in, A symbol representing a point in time within a unit of time. The first digit representing the catalytic degradation efficiency of the reaction The overall reactivity value at each time point The weighting coefficients represent the spectral absorbance. Indicates the first Normalized spectral absorbance values ​​at each time point The weighting coefficients representing potential fluctuations. Indicates the first The normalized potential fluctuation values ​​at each time point.

[0108] S6. Compare the catalytic process curve with the preset target curve to determine whether the magnetic field control parameters need to be adjusted, and complete the real-time purification control of tap water.

[0109] Based on the water purification requirements, a target curve is set, and the catalytic process curve and the target curve are compared point by point on the same time axis.

[0110] Specifically, a target curve is set based on the water purification requirements. The target curve refers to the trend of pollutant removal efficiency over time under the purification conditions of water purification requirements (physical, chemical, and microbiological indicators for safe drinking or specific uses). The trend is determined based on historical purification efficiency and purification targets. The currently generated catalytic process curve and the target curve are placed on the same time axis, and the catalytic process curve and the target curve are compared point by point over time. The point-by-point comparison is to compare the difference between the actual catalytic efficiency value of the catalytic process curve and the expected catalytic efficiency value of the target curve at each time point.

[0111] If the shape of the catalytic process curve is inconsistent with that of the target curve, the magnetic field control parameters will be adjusted to correct the catalytic process curve.

[0112] After completing the time-point comparison, if significant deviations are found between the catalytic process curve and the target curve at multiple time points, a significant deviation is defined as the absolute difference between the actual catalytic efficiency value and the expected catalytic efficiency value of the target curve at three or more consecutive time points exceeding the preset allowable range. The preset allowable range is set based on water quality purification standards and historical operating data; for example, it is typically ±5% of the expected catalytic efficiency value. This preset allowable range ensures that while maintaining high purification efficiency, a certain degree of normal fluctuation is permitted, avoiding frequent adjustments to the magnetic field control parameters due to minor fluctuations. This ensures overall stability and reliability. Inconsistent curve shapes, such as slow and drastic increases in catalytic efficiency, indicate that the current catalytic process has not reached the ideal state, triggering an adjustment mechanism for the magnetic field control parameters. This mechanism includes optimizing the magnetic field strength value and pollutant migration direction in the magnetic field's active area. The optimization process involves adjusting the magnetic field strength value in the active area to enhance catalytic activity based on the deviation between the catalytic progress curve and the target curve, and adjusting the direction of the magnetic field according to the actual migration path of the pollutants to ensure that the branched catalytic clusters can more effectively capture and degrade pollutants. This, in turn, adjusts the catalytic degradation process, causing the catalytic progress curve to gradually approach the target curve.

[0113] If the catalytic process curve is consistent with the target curve, then continue to maintain real-time purification of tap water until the tap water purification control is completed.

[0114] During the time-point comparison, if the catalytic efficiency value of the catalytic process curve and the target curve remains highly consistent in the overall trend, it indicates that the current catalytic degradation process has achieved the expected purification effect. There is no need to adjust the magnetic field control parameters. Continue to maintain the existing magnetic field control parameters and catalytic degradation state. Under the catalytic degradation state, the branched catalytic clusters continue to efficiently degrade pollutants in the unpurified water source. The electrochemical sensor continues to monitor the catalytic degradation area in real time and continuously updates the catalytic process curve. The entire real-time purification process of tap water operates stably under the closed-loop feedback mechanism until the purification treatment of all water sources is completed, realizing the dynamic regulation and precise control of tap water quality.

[0115] This embodiment also provides a computer device applicable to the real-time water purification control method based on edge computing, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the real-time water purification control method based on edge computing as proposed in the above embodiment.

[0116] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0117] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the edge computing-based real-time water purification control method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0118] In summary, this invention achieves adaptive real-time purification of heavy metal pollution through the synergistic construction of a three-dimensional pollutant map and closed-loop control of the catalytic process. Within a fixed time window, the attenuation gradient and displacement vector of the optical feature data stream are extracted and spatially convolutionally processed to generate a three-dimensional pollutant map. This accurately quantifies the spatial distribution gradient and migration direction of pollutants, providing a dynamic modeling basis for the division of the magnetic field's active area. Based on the magnetic field control parameters generated from the map, a rotating magnetic field is driven to form a branched catalytic cluster. This multi-level branching structure significantly increases the density of active sites. Furthermore, an electrochemical sensor synchronously monitors the catalytic degradation region to generate a catalytic process curve. The magnetic field parameters are dynamically adjusted based on the target curve's fit, forming a millisecond-level closed-loop control from pollutant localization and cluster structure optimization to degradation feedback, ensuring the consistency between purification efficiency and water quality safety.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A real-time tap water purification and control method based on edge computing, characterized in that: include, Acquire the offset fluorescence wavelength and attenuation intensity characteristics to generate an optical feature data stream; The attenuation gradient and displacement vector of the optical feature data stream are extracted within a fixed time window. The attenuation gradient and displacement vector are then spatially convolved to construct a three-dimensional pollutant map. Based on the pollutant distribution in the three-dimensional pollutant map, a vector distribution pattern is extracted. The magnetic field area and magnetic field strength are set according to the vector distribution pattern, and the magnetic field control parameters are output. The magnetic field control parameters are input to the magnetic field generator. The magnetic field generator matches the corresponding magnetic field strength according to the magnetic field control parameters to generate a rotating magnetic field. Vortex motion occurs in the rotating magnetic field to generate branched catalytic clusters. Unpurified water is catalytically degraded using branched catalytic clusters, while the spectral absorbance and potential fluctuations of the catalytic degradation region are monitored in real time using electrochemical sensors to generate a catalytic process curve. By comparing the degree of agreement between the catalytic process curve and the preset target curve, it can be determined whether the magnetic field control parameters need to be adjusted to complete the real-time purification control of tap water. The specific steps for acquiring the offset fluorescence wavelength and attenuation intensity characteristics, and generating an optical feature data stream, are as follows: Integrating quantum dot films into the pipeline monitoring section of water treatment pipelines; A laser beam is emitted to irradiate a quantum dot film, exciting the quantum dot film to generate a fluorescence signal; Feature extraction is performed on the fluorescence signal to obtain the offset fluorescence wavelength and attenuation intensity characteristics; The offset fluorescence wavelength and attenuation intensity characteristics are compared with the standard fluorescence spectrum to output an optical characteristic data stream; The vortex motion in the rotating magnetic field refers to the pre-placed nanoparticles moving in a spiral trajectory along the axis of rotation of the rotating magnetic field under the influence of the rotating magnetic field and driven by the periodic change in the direction of the rotating magnetic field.

2. The real-time tap water purification and control method based on edge computing according to claim 1, characterized in that: The specific steps for extracting the attenuation gradient and displacement vector of the optical feature data stream within a fixed time window, performing spatial convolution on the attenuation gradient and displacement vector, and constructing a three-dimensional pollutant map are as follows: Within a fixed time window, the absolute value of the difference between the fluorescence intensity attenuation values ​​at adjacent time points in the optical feature data stream is extracted to generate an attenuation gradient sequence; The difference between the offset fluorescence wavelengths of adjacent spatial points in the optical feature data stream is extracted to generate a displacement vector sequence. Map the decay gradient sequence and the displacement vector sequence to decay gradient matrix and displacement vector matrix, respectively; A sliding window algorithm is used to perform pointwise convolution on the decay gradient matrix and the displacement vector matrix to generate a spatial correlation matrix. The spatial correlation matrix is ​​overlaid with the timestamps and spatial coordinates in the optical feature data stream to form three-dimensional environmental data. Visualization tools are used to perform spatiotemporal intensity visualization mapping of 3D environmental data, outputting a 3D pollutant map.

3. The real-time tap water purification and control method based on edge computing according to claim 1, characterized in that: The specific steps are as follows: extracting a vector distribution pattern map from the pollutant distribution in the three-dimensional pollutant map, setting the magnetic field area and magnetic field strength according to the vector distribution pattern map, and outputting magnetic field control parameters. Pollutant distribution information of displacement vector sequence is extracted from three-dimensional pollutant map, and spatial correlation of pollutant distribution information is performed to generate vector distribution pattern map; Based on the pollutant migration direction and pollutant distribution area in the vector distribution pattern diagram, the pipeline monitoring section is divided into multiple magnetic field action areas, and the boundary coordinates of the magnetic field action areas are recorded. Set the magnetic field strength value according to the vector amplitude of each magnetic field's area of ​​action; The boundary coordinates, magnetic field strength values, and pollutant migration directions of each magnetic field's active region are combined to form magnetic field control parameters.

4. The real-time tap water purification and control method based on edge computing according to claim 1, characterized in that: The steps involve inputting magnetic field control parameters to a magnetic field generator, which then matches the corresponding magnetic field strength according to the control parameters to generate a rotating magnetic field. Input the magnetic field control parameters into the magnetic field generator; The current input intensity and energizing sequence of the electromagnetic coil array in the magnetic field generator are adjusted by controlling the magnetic field parameters. After completing the current input intensity adjustment and energizing timing settings, start the electromagnetic coil array and activate the electromagnetic coils in the array according to the energizing timing to generate a rotating magnetic field.

5. The real-time tap water purification and control method based on edge computing according to claim 1, characterized in that: The process involves catalytic degradation of unpurified water sources using branched catalytic clusters, while simultaneously monitoring the spectral absorbance and potential fluctuations of the catalytic degradation region in real time using electrochemical sensors to generate a catalytic progress curve. The specific steps are as follows: Unpurified water sources are catalytically degraded using branched catalytic clusters, and the catalytic degradation zone is output. An electrochemical sensor monitors the spectral absorbance and potential fluctuations in the catalytic degradation region in real time and outputs spectral absorbance and potential fluctuation signals. The spectral absorbance signal and the potential fluctuation signal are time-synchronized fusion processed to generate a time-intensity correlated signal sequence; The time-intensity correlated signal sequence is normalized, and the catalytic degradation efficiency is mapped onto the normalized time-intensity correlated signal sequence to generate a catalytic process curve.

6. The real-time tap water purification and control method based on edge computing according to claim 1, characterized in that: The comparison of the catalytic process curve with the preset target curve determines whether the magnetic field control parameters need adjustment to achieve real-time tap water purification control. The specific steps are as follows: Based on the water purification requirements, a target curve is set, and the catalytic process curve and the target curve are compared point by point on the same time axis. If the shape of the catalytic process curve is inconsistent with that of the target curve, the magnetic field control parameters will be adjusted to correct the catalytic process curve. If the catalytic process curve matches the target curve, then continue to maintain real-time purification of tap water until the tap water purification control is completed.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the real-time tap water purification and control method based on edge computing as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the real-time tap water purification and control method based on edge computing as described in any one of claims 1 to 6.

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