Intelligent water treatment purification method and system for environmental engineering

Abstract

The present application relates to the field of environmental engineering water treatment technology, and more particularly to an intelligent water treatment and purification method and system for environmental engineering. Based on real-time monitoring data, a pollutant space-time field structure is constructed, and sub-regions are divided accordingly. A cross-regional purification coordination matrix is established. Through multi-scale decomposition of the space-time field, the response weight of each sub-region to different components is determined, and finally the basic purification strategy and the response purification strategy are generated and superimposed to form a coordinated execution plan. The present application realizes adaptive and accurate purification of dynamic distribution of pollutants, and improves the overall treatment efficiency and coordination.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology in environmental engineering, and in particular to intelligent water treatment and purification methods and systems for environmental engineering. Background Technology

[0002] In the field of water treatment technology, especially for water purification in environmental engineering, conventional methods usually rely on pre-set fixed process flows or feedback control based on limited monitoring points. These methods generally adopt uniform treatment intensity and schedule. For example, in sewage treatment plants, aeration, chemical dosing, and other processes are often operated according to fixed time sequences and dosages, or simply adjusted proportionally based on a few key parameters at the inlet, such as chemical oxygen demand and ammonia nitrogen concentration.

[0003] Conventional practices have significant limitations. Due to a lack of in-depth understanding of the spatiotemporal distribution details of pollutants within treatment ponds or natural water bodies, fixed or simple feedback-based purification strategies are ill-suited to the uneven distribution and dynamic changes of pollutants. The treatment process is prone to both "overtreatment" and "undertreatment": energy and reagents are wasted in areas with low pollutant concentrations, while purification is insufficient in areas where pollutants accumulate or suddenly surge, resulting in low overall treatment efficiency and high energy and material consumption.

[0004] Existing methods for treating complex water bodies typically treat the entire water body as a homogeneous unit or perform simple physical partitioning, neglecting the mutual transport and influence of pollutants between different areas. The operation of each treatment unit or area is often independent or weakly correlated, lacking system-level synergistic optimization. This "lone wolf" approach cannot achieve optimal spatial allocation of purification resources. When a pollution load surge occurs in one area, it is impossible to effectively mobilize the purification capacity of other areas for coordinated buffering and response, resulting in poor system resilience and overall purification stability. Summary of the Invention

[0005] The present invention provides an intelligent water treatment and purification method and system for environmental protection projects, which can solve the problems in the prior art.

[0006] A first aspect of the present invention provides an intelligent water treatment and purification method for environmental engineering, comprising: Based on real-time monitoring data of the water body to be treated, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field. Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification treatment sub-regions; by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification treatment sub-regions, the correlation between the purification intensity of each purification treatment sub-region and the pollution load change rate of adjacent purification treatment sub-regions is established, and a cross-regional purification synergy matrix is ​​obtained. The spatiotemporal field structure is decomposed into a multi-scale decomposition, and the pollutant concentration gradient field is decomposed into a background field component that characterizes the macroscopic distribution trend and a perturbation field component that characterizes the abrupt change characteristics of local pollutant concentration; based on the cross-regional purification coordination matrix, the response weights of each purification treatment sub-region to the background field component and the perturbation field component are determined. For the background field component and the disturbance field component, a continuous basic purification strategy and an impulse response purification strategy are generated according to the response weights and then superimposed in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

[0007] Based on real-time monitoring data of the water body to be treated, by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field, the spatiotemporal field structure describing the evolution of pollutant distribution is obtained, including: Based on the pollutant concentration distribution data and water flow characteristic data in the real-time monitoring data, the pollutant concentration gradient vector at each spatial location in the water body to be treated is calculated to obtain the pollutant concentration gradient field. The flow velocity vector and flow direction information at each spatial location in the water body to be treated are extracted to obtain the flow velocity field. By performing a vector synthesis operation on the spatially corresponding pollutant concentration gradient vector and the flow velocity vector, the comprehensive transport vector field of pollutants under the dual effects of concentration gradient driving and flow velocity carrying is calculated, and the pollutant transport rate distribution and pollutant transport direction distribution are obtained. Based on the pollutant transport rate distribution, by calculating the rate of change of concentration gradient between adjacent spatial locations in the pollutant concentration gradient field, the spatial boundary where the rate of change of concentration gradient exceeds the preset gradient abruptness judgment threshold is identified, and the location of the concentration gradient abruptness boundary is obtained. Based on the pollutant transport direction distribution, by tracing the continuous distribution path of the flow velocity vector in space, spatial channel regions with consistent flow direction and flow rate higher than a preset flow intensity threshold are identified, thereby obtaining the flow direction characteristics of the pollutant transport channel. By spatially superimposing the pollutant concentration gradient field, the flow velocity field, the location of the concentration gradient abrupt change boundary, and the flow direction characteristics of the pollutant transport channel, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained.

[0008] Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification sub-regions, including: Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel, the angle between the flow direction vector of the pollutant transport channel and the normal vector of the concentration gradient abrupt change boundary location at each concentration gradient abrupt change boundary location is calculated to obtain the flow crossing angle distribution. Based on the flow crossing angle distribution, the location of the concentration gradient abrupt change boundary where the flow crossing angle is less than the preset crossing judgment angle threshold is identified as the active cross-border boundary of pollutants, and the location of the concentration gradient abrupt change boundary where the flow crossing angle is greater than the preset crossing judgment angle threshold is identified as the barrier boundary of pollutants. Based on the active crossing boundary and the barrier boundary of the pollutants, a pollutant transmission impedance field is constructed for the water body to be treated. By calculating the shortest transmission impedance distance from each spatial location in the pollutant transmission impedance field to the barrier boundary of the pollutants, the impedance distance distribution of each spatial location is obtained. According to the impedance distance distribution, spatial locations where the difference of the shortest transmission impedance distance is less than a preset distance clustering threshold are clustered into the same purification treatment sub-region.

[0009] By quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions is established, resulting in a cross-regional purification synergy matrix, which includes: Based on the spatial adjacency relationship of the multiple purification sub-regions, purification sub-regions that have spatial boundary contact are regarded as adjacent purification sub-region pairs. Based on the pollutant concentration gradient and flow velocity vector at the shared boundary between the adjacent purification sub-regions, the pollutant mass transported across the boundary per unit time is calculated as the pollutant mass exchange flux between the adjacent purification sub-regions. Based on the pollutant mass exchange flux, the influence coefficient of the change in purification intensity of the source purification treatment sub-region on the pollution load change rate of the target purification treatment sub-region is calculated as the purification capacity coupling coefficient. Based on the pollutant mass exchange flux and the purification capacity coupling coefficient, the transmission path and transmission intensity of the change in purification intensity of the purification treatment sub-region on the pollution load change rate of adjacent purification treatment sub-regions are quantified, and the correlation between purification intensity and pollution load change rate is obtained. The correlation between the purification intensity and the pollution load change rate of all purification sub-regions is arranged in matrix form according to the spatial index order of the purification sub-regions to obtain the cross-regional purification coordination matrix.

[0010] The spatiotemporal field structure is decomposed into multiple scales, and the pollutant concentration gradient field is decomposed into a background field component characterizing the macroscopic distribution trend and a perturbation field component characterizing the abrupt change characteristics of local pollutant concentration, including: Spatial spectrum analysis is performed on the pollutant concentration gradient field in the spatiotemporal field structure to transform the pollutant concentration gradient field from the spatial domain to the frequency domain, thereby obtaining the spatial spectrum distribution of the pollutant concentration gradient field. Calculate the energy proportion of each frequency component in the spatial spectrum distribution, and identify the cutoff frequency corresponding to the energy accumulation proportion in the spatial spectrum distribution reaching a preset energy accumulation threshold as the background field cutoff frequency. A spatial low-pass filter is constructed, and the cutoff frequency parameter of the spatial low-pass filter is set to the background field cutoff frequency. The spatial low-pass filter is used to perform spatial low-frequency filtering on the pollutant concentration gradient field, and low-frequency components with frequencies lower than the background field cutoff frequency in the pollutant concentration gradient field are extracted to obtain the background field component that characterizes the macroscopic distribution trend. Calculate the numerical difference between the pollutant concentration gradient field and the background field component at each spatial location point, extract the high-frequency component in the pollutant concentration gradient field whose frequency is higher than the cutoff frequency of the background field, and obtain the perturbation field component that characterizes the local pollutant concentration abrupt change.

[0011] Based on the cross-regional purification coordination matrix, the response weights of each purification sub-region to the background field component and the disturbance field component are determined as follows: Based on the magnitude of the coupling coefficient between the current purification sub-region and each adjacent purification sub-region, the set of adjacent purification sub-regions that affect the current purification sub-region is identified as the set of coupling influence source regions. Extract the background field component and disturbance field component of each coupled influence source region in the set of coupled influence source regions, calculate the spatial variation amplitude of pollutant concentration gradient value in the background field component as the background field variation amplitude, and calculate the spatial variation amplitude of pollutant concentration gradient value in the disturbance field component as the disturbance field variation amplitude. The sum of the background field change amplitude and the disturbance field change amplitude is calculated as the total change amplitude. The ratio of the background field change amplitude to the total change amplitude is calculated as the background field change contribution ratio. The ratio of the disturbance field change amplitude to the total change amplitude is calculated as the disturbance field change contribution ratio. The response weights of the current purification sub-region to the background field components are calculated based on the purification capacity coupling coefficient and the contribution ratio of the background field change. The response weights of the current purification sub-region to the disturbance field components are also calculated based on the purification capacity coupling coefficient and the contribution ratio of the disturbance field change.

[0012] For the background field component and the disturbance field component, a continuous basic purification strategy and an impulse response purification strategy are generated according to the response weights and then superimposed in time series to form a multi-timescale collaborative intelligent water treatment purification execution scheme, including: Based on the response weight of the current purification sub-region to the background field component, the purification load benchmark value corresponding to the background field component is calculated. The purification intensity parameter and purification duration parameter are determined according to the purification load benchmark value, and a continuous basic purification strategy is generated to purify the background field component with constant purification intensity in a continuous time period. Based on the response weights of the current purification sub-regions to the disturbance field components, the peak value of the purification load corresponding to the disturbance field components is calculated. The peak value of the purification load is used to determine the peak value of the purification and the peak value of the purification triggering time, and a pulse-type response purification strategy is generated to be activated at the moment of sudden change of the disturbance field components. The time-series purification intensity curve of the continuous basic purification strategy is constructed based on the purification intensity parameter and the purification duration parameter; the time-series purification intensity curve of the pulse response purification strategy is constructed based on the purification peak intensity parameter and the purification trigger time parameter. The time-series purification intensity curve of the continuous basic purification strategy is used as the baseline of the basic purification intensity, and the time-series purification intensity curve of the pulse-type response purification strategy is used as the purification intensity increment superimposed on the baseline of the basic purification intensity. By superimposing them on the time axis, a smart water treatment purification execution scheme with multi-time scale coordination is obtained.

[0013] A second aspect of the present invention provides an intelligent water treatment and purification system for environmental engineering, comprising: The monitoring and analysis unit is used to obtain the spatiotemporal field structure describing the evolution of pollutant distribution by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field based on real-time monitoring data of the water body to be treated. The region division unit is used to divide the water body to be treated into multiple purification treatment sub-regions based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel in the spatiotemporal field structure. The collaborative matrix unit is used to establish the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, and to obtain the cross-regional purification collaborative matrix. Multi-scale sub-units are used to decompose the spatiotemporal field structure into multi-scale components, decomposing the pollutant concentration gradient field into background field components characterizing the macroscopic distribution trend and perturbation field components characterizing the abrupt change characteristics of local pollutant concentration. The weight determination unit is used to determine the response weights of each purification sub-region to the background field component and the disturbance field component based on the cross-regional purification coordination matrix. The strategy generation unit is used to generate a continuous basic purification strategy and a pulse response purification strategy for the background field component and the disturbance field component according to the response weight, and then superimpose them in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

[0014] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0016] This method can construct the coupling relationship between the pollutant concentration gradient field and the flow velocity field based on real-time monitoring data, thereby accurately describing the distribution and evolution of pollutants in the spatiotemporal dimensions. By analyzing the abrupt boundary of the concentration gradient and the characteristics of pollutant transport channels in the spatiotemporal field structure, the water body can be scientifically divided into multiple purification and treatment sub-regions. This division method can accurately reflect the migration path of pollutants between regions, laying a precise spatial foundation for subsequent coordinated purification and control.

[0017] By quantifying the coupling coefficient between pollutant mass exchange flux and purification capacity between adjacent sub-regions, a dynamic correlation model between the purification intensity of each sub-region and the pollution load change rate of adjacent regions was established, forming a cross-regional purification coordination matrix. This matrix can effectively characterize the mutual influence of purification operations in different regions, enabling purification strategies to take a global perspective, consider the transfer of pollution load between regions, and avoid the problem of overall inefficiency caused by local optimization.

[0018] The pollutant concentration gradient field is decomposed into a multi-scale component, separating it into a background field component representing macroscopic trends and a perturbation field component representing local abrupt changes. Based on the synergy matrix, different response weights for these two components are determined for each sub-region, enabling the purification strategy to specifically differentiate pollution characteristics at different scales. This allows the system to simultaneously address both large-scale pollution diffusion trends and localized, sudden pollution events.

[0019] Based on the response weights, a continuous basic purification strategy and an impulse response purification strategy are generated for the background field component and the disturbance field component, respectively. These two strategies are then superimposed temporally to form a multi-timescale collaborative intelligent execution scheme. This scheme achieves an organic combination of long-term stable purification and short-term rapid response. While ensuring overall processing efficiency, it significantly improves the system's adaptability and response speed to dynamic changes in pollution, thereby realizing the optimized allocation and efficient utilization of purification resources in the spatiotemporal dimensions. Attached Figure Description

[0020] Figure 1 A schematic diagram of a smart water treatment and purification method for environmental protection projects; Figure 2 This is a schematic diagram of the process for generating a multi-timescale collaborative purification strategy. Detailed Implementation

[0021] 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, and 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.

[0022] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0023] Figure 1 This is a schematic flowchart of an intelligent water treatment and purification method for environmental engineering according to an embodiment of the present invention, as shown below. Figure 1 As shown, intelligent water treatment and purification methods used in environmental protection projects include: Based on real-time monitoring data of the water body to be treated, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field. Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification treatment sub-regions. By quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions is established, and a cross-regional purification synergy matrix is ​​obtained. The spatiotemporal field structure is decomposed into multi-scale components, and the pollutant concentration gradient field is decomposed into background field components that characterize the macroscopic distribution trend and perturbation field components that characterize the abrupt change characteristics of local pollutant concentration. Based on the cross-regional purification collaboration matrix, the response weights of each purification sub-region to the background field component and the disturbance field component are determined. For the background field component and the disturbance field component, a continuous basic purification strategy and an impulse response purification strategy are generated according to the response weights and then superimposed in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

[0024] In one optional implementation, based on real-time monitoring data of the water body to be treated, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained by establishing a coupling relationship between the pollutant concentration gradient field and the flow velocity field, including: Based on the pollutant concentration distribution data and water flow characteristic data in the real-time monitoring data, the pollutant concentration gradient vector at each spatial location in the water body to be treated is calculated to obtain the pollutant concentration gradient field. The flow velocity vector and flow direction information at each spatial location in the water body to be treated are extracted to obtain the flow velocity field. By performing a vector synthesis operation on the spatially corresponding pollutant concentration gradient vector and the flow velocity vector, the comprehensive transport vector field of pollutants under the dual effects of concentration gradient driving and flow velocity carrying is calculated, and the pollutant transport rate distribution and pollutant transport direction distribution are obtained. Based on the pollutant transport rate distribution, by calculating the rate of change of concentration gradient between adjacent spatial locations in the pollutant concentration gradient field, the spatial boundary where the rate of change of concentration gradient exceeds the preset gradient abruptness judgment threshold is identified, and the location of the concentration gradient abruptness boundary is obtained. Based on the pollutant transport direction distribution, by tracing the continuous distribution path of the flow velocity vector in space, spatial channel regions with consistent flow direction and flow rate higher than a preset flow intensity threshold are identified, thereby obtaining the flow direction characteristics of the pollutant transport channel. By spatially superimposing the pollutant concentration gradient field, the flow velocity field, the location of the concentration gradient abrupt change boundary, and the flow direction characteristics of the pollutant transport channel, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained.

[0025] Real-time monitoring data of the water body to be treated is acquired by deploying multi-parameter water quality monitoring sensors at different spatial locations within the water body. The types of data collected by the sensors include pollutant concentration values, water flow velocity, flow direction angle, and the three-dimensional spatial coordinates of the monitoring points. The sensor deployment density is determined based on the size of the water body; for water bodies ranging from 1000 square meters to 5000 square meters, the sensor spacing is set to 5 to 15 meters to ensure the capture of spatial variations in pollutant concentrations. The data acquisition frequency is set to a complete sampling every 30 to 120 seconds. The collected raw data undergoes outlier removal and data smoothing before being used for subsequent calculations.

[0026] Pollutant concentration distribution data is presented in the form of pollutant mass concentration values ​​at each monitoring point, using spatial coordinates. The pollutant concentration at the location was recorded as follows A discrete concentration distribution dataset is constructed. Water flow characteristic data includes the flow velocity at each monitoring point. and flow direction angle The flow direction angle is measured clockwise with true north as the reference. When calculating the pollutant concentration gradient vector, for any point in space, concentration data from the 6 to 8 nearest monitoring points are selected. The least squares method is used to fit the spatial distribution function of the concentration at that location, and the partial derivative of the fitted function is calculated to obtain the concentration gradient vector at that location. The concentration gradient vector points in the direction of the fastest increase in concentration, and the magnitude of the vector represents the degree of intensity of the concentration change. Combining the concentration gradient vectors of all spatial locations forms a pollutant concentration gradient field, which describes the distribution and variation of pollutant concentration in three-dimensional space in the form of a vector field.

[0027] The flow velocity at each monitoring point and flow direction angle The velocity vector components are converted to Cartesian coordinates. The horizontal component is calculated using trigonometric functions, while the vertical component is determined based on the depth stratification of the water body. For shallow water, the vertical velocity component is small and can be ignored; for water deeper than 2 meters, monitoring points need to be set up at different depths to capture vertical flow characteristics. The velocity vectors at all locations are aggregated to form a flow velocity field, which reflects the internal flow dynamics of the water body.

[0028] Vector composition operations are performed according to the spatial correspondence. For each computational grid point in space, the pollutant concentration gradient vector at that point is extracted. and flow velocity vector The comprehensive transport vector of pollutants at a given point is calculated. The calculation considers two transport mechanisms: diffusion transport driven by concentration gradients and convective transport carried by flow velocity. The contribution of diffusion transport is proportional to the magnitude of the concentration gradient vector, while the contribution of convective transport is proportional to the magnitude of the flow velocity vector. The two transport vectors are then superimposed according to their respective weighting coefficients, which are determined based on the relative magnitudes of the water body's diffusion coefficient and flow intensity. When the flow velocity is greater than 0.1 m / s, convective transport dominates, and its weighting coefficient is set to 0.7 to 0.9; when the flow velocity is less than 0.05 m / s, the effect of diffusion transport is enhanced, and its weighting coefficient is set to 0.6 to 0.8. After the comprehensive transport vector field is calculated, the magnitude of the comprehensive transport vector at each location is extracted as the pollutant transport rate, and the direction angle of the vector is extracted as the pollutant transport direction, forming the pollutant transport rate distribution and the pollutant transport direction distribution.

[0029] The rate of change of concentration gradient between adjacent spatial locations is calculated along the direction of the concentration gradient vector. Specifically, adjacent points with a spatial interval of 0.5 meters to 2 meters are selected in the concentration gradient field. The difference in the magnitude of the concentration gradient vector at these two points is calculated, and this difference is divided by the spatial distance between the two points to obtain the rate of change of concentration gradient. When the rate of change of concentration gradient exceeds a preset gradient abrupt change threshold, a drastic change in concentration gradient is determined at that location. The gradient abrupt change threshold is set based on the background concentration level of pollutants in the water body to be treated. For water bodies with a background concentration of 10 mg / L to 50 mg / L, the gradient abrupt change threshold is set to 5 mg / L / m to 20 mg / L / m. All spatial locations that meet the abrupt change criteria are marked. Connecting these points spatially forms the concentration gradient abrupt change boundary, which often corresponds to the boundary line of pollutant concentration distribution or the edge region of the pollution source.

[0030] Pollutant transport channels are identified by tracking the spatial continuity of the flow velocity vector field. Points with flow velocities exceeding a preset flow intensity threshold are selected as starting points for tracking. This threshold is set based on the average flow velocity of the water body, typically 1.5 to 2.5 times the average velocity. Starting from the starting point, the system advances one step distance (1 to 3 meters) along the flow velocity vector direction. Upon reaching a new location, the flow velocity vector at that location is extracted, and the angular deviation between the flow direction at the new location and the previous location is calculated. If the angular deviation is less than 15 to 30 degrees, the flow direction is considered consistent, and the tracking continues along the flow direction to the next location. This tracking process is repeated until the flow velocity drops below the threshold or the angular deviation exceeds the set angle. All locations along the tracking path are then connected to form a transport channel. Tracking operations are performed on multiple high-velocity regions in the flow velocity field to obtain multiple pollutant transport channels. The dominant flow direction of each channel is extracted as its flow direction feature.

[0031] The spatiotemporal field structure is constructed by superimposing the pollutant concentration gradient field, flow velocity field, concentration gradient abrupt change boundary location, and flow direction characteristics of pollutant transport channels in the same spatial coordinate system. During the superposition process, the concentration gradient field and flow velocity field are stored in the form of vector field data, the concentration gradient abrupt change boundary is marked in the geometric form of spatial curves or surfaces, and the pollutant transport channels are embedded in the form of directed paths.

[0032] In one optional implementation, based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification sub-regions, including: Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel, the angle between the flow direction vector of the pollutant transport channel and the normal vector of the concentration gradient abrupt change boundary location at each concentration gradient abrupt change boundary location is calculated to obtain the flow crossing angle distribution. Based on the flow crossing angle distribution, the location of the concentration gradient abrupt change boundary where the flow crossing angle is less than the preset crossing judgment angle threshold is identified as the active cross-border boundary of pollutants, and the location of the concentration gradient abrupt change boundary where the flow crossing angle is greater than the preset crossing judgment angle threshold is identified as the barrier boundary of pollutants. Based on the active crossing boundary and the barrier boundary of the pollutants, a pollutant transmission impedance field is constructed for the water body to be treated. By calculating the shortest transmission impedance distance from each spatial location in the pollutant transmission impedance field to the barrier boundary of the pollutants, the impedance distance distribution at each spatial location is obtained. Based on the impedance distance distribution, spatial locations where the difference in the shortest transmission impedance distance is less than a preset distance clustering threshold are clustered into the same purification treatment sub-region.

[0033] For each concentration gradient abrupt change boundary location identified in the spatiotemporal field structure, the flow direction information of the pollutant transport channel at that location is extracted. The flow direction vector can be represented as... ,in , , These represent the components of the flow velocity field along the three spatial coordinate axes. Simultaneously, the normal vector at the boundary of the abrupt concentration gradient change is calculated. The normal vector points in the direction of increasing concentration. The flow crossing angle is obtained by calculating the angle between the flow direction vector and the normal vector. The calculation of this angle is achieved using the relationship between the vector dot product and the vector magnitude, specifically through... Determine the cosine of the included angle to obtain the angle value. Repeat this calculation process at different locations along the abrupt boundary of the concentration gradient to form the spatial distribution characteristics of the flow crossing angle.

[0034] The physical significance of the flow crossing angle lies in characterizing the ease with which pollutants cross a boundary where the concentration gradient abruptly changes. When the flow crossing angle is small, the flow direction is approximately parallel to the concentration gradient direction, indicating that pollutants can easily cross the boundary along the flow direction and enter adjacent areas at this boundary location. Conversely, when the flow crossing angle is large, the flow direction is approximately perpendicular to or even opposite to the concentration gradient direction, indicating that this boundary location acts as a barrier to pollutant transport. A preset crossing angle threshold is set as follows: Its typical value range is 45° to 60°. The flow crossing angle is less than... The boundary locations are marked as active boundary crossings by pollutants; these boundary locations are the main channels for pollutant migration between different areas. The flow crossing angle is greater than... The boundary locations are marked as pollutant barrier boundaries, which naturally separate the water body to be treated into relatively independent areas.

[0035] After identifying pollutants actively crossing boundaries and pollutant barrier boundaries, a pollutant transport impedance field is constructed for the water body to be treated. The transport impedance field is a scalar field, and its value at each point in space reflects the ease with which pollutants diffuse or transport from that point to the surrounding area. At pollutant barrier boundaries, the transport impedance value is set to a larger value, reflecting the difficulty for pollutants to cross the boundary; at locations where pollutants actively cross boundaries, the transport impedance value is set to a smaller value, reflecting the ease with which pollutants migrate through the boundary; in ordinary areas within the water body, the transport impedance value is determined based on the local flow velocity and diffusion coefficient, with smaller values ​​at locations with higher flow velocities or higher diffusion coefficients.

[0036] Based on the constructed pollutant transport impedance field, the shortest transport impedance distance from each spatial location in the water body to be treated to the pollutant barrier boundary is calculated. This distance differs from the conventional Euclidean geometric distance; instead, it is a weighted distance that considers the pollutant transport characteristics. Specifically, starting from any spatial location in the water body to be treated, a path to the nearest pollutant barrier boundary is searched along all possible paths. For each path, the transport impedance value is integrated along the path to obtain the cumulative transport impedance of that path. Among all possible paths, the path with the minimum cumulative transport impedance is selected, and its corresponding cumulative impedance value is the shortest transport impedance distance at that spatial location. By traversing all spatial locations of the water body to be treated, the impedance distance distribution is obtained. .

[0037] Impedance distance distribution reveals the degree of transport connectivity between different regions of the water body under treatment and the pollutant barrier boundary. Spatial locations with similar shortest transport impedance distances typically exhibit similar pollutant transport characteristics and treatment requirements. A preset distance clustering threshold is set. Its typical value ranges from 5% to 15% of the impedance distance distribution range. For any two spatial locations in the water body to be treated, if the difference in their shortest transmission impedance distance satisfies... If the two spatial locations are similar, they are assigned to the same purification sub-region. A spatial clustering algorithm is used to identify all spatial locations that meet the condition of similar impedance distance, forming a purification sub-region. This process is repeated until all spatial locations of the water body to be treated are assigned to their respective purification sub-regions.

[0038] This region division method, based on flow crossing angle and transmission impedance distance, ensures relatively consistent pollutant transport characteristics within each treatment sub-region. Different sub-regions are connected by pollutant-barrier boundaries or active crossing boundaries, facilitating the development of differentiated treatment strategies tailored to the characteristics of each sub-region. The number of resulting treatment sub-regions typically ranges from 3 to 15, depending on the complexity of the water body and the distribution of abrupt concentration gradient boundaries. The spatial extent and shape of each treatment sub-region are naturally determined by the impedance distance distribution characteristics of its internal spatial location, eliminating the need for pre-defined boundaries and ensuring the objectivity and rationality of the region division.

[0039] In one optional implementation, by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions is established, resulting in a cross-regional purification synergy matrix including: Based on the spatial adjacency relationship of the multiple purification sub-regions, purification sub-regions that have spatial boundary contact are regarded as adjacent purification sub-region pairs. Based on the pollutant concentration gradient and flow velocity vector at the shared boundary between the adjacent purification sub-regions, the pollutant mass transported across the boundary per unit time is calculated as the pollutant mass exchange flux between the adjacent purification sub-regions. Based on the pollutant mass exchange flux, the influence coefficient of the change in purification intensity of the source purification treatment sub-region on the pollution load change rate of the target purification treatment sub-region is calculated as the purification capacity coupling coefficient. Based on the pollutant mass exchange flux and the purification capacity coupling coefficient, the transmission path and transmission intensity of the change in purification intensity of the purification treatment sub-region on the pollution load change rate of adjacent purification treatment sub-regions are quantified, and the correlation between purification intensity and pollution load change rate is obtained. The correlation between the purification intensity and the pollution load change rate of all purification sub-regions is arranged in matrix form according to the spatial index order of the purification sub-regions to obtain the cross-regional purification coordination matrix.

[0040] After dividing the water body into sub-regions, it is necessary to establish the coupling relationship between each purification sub-region to support subsequent coordinated purification control. The core of this process lies in quantifying the pollutant transport characteristics between adjacent regions and the linkage effect of purification operations.

[0041] After clarifying the spatial locations of multiple treatment sub-regions, pairs of directly interacting regions are identified. The criterion for determining whether two treatment sub-regions are adjacent is whether their spatial boundaries are in contact. For sub-region boundaries represented by vector polygons, the geometric relationship between polygon boundary segments is calculated to determine if shared boundary segments exist. When the boundary segments of two treatment sub-regions overlap or the distance is less than a preset threshold, the region pair is marked as an adjacent treatment sub-region pair. For regular grid divisions, adjacency can be directly determined based on the difference in grid indices. In actual treatment, the anisotropic characteristics of water flow need to be considered. Even if two regions are in spatial contact, if the flow direction is basically parallel to the boundary, the pollutant exchange intensity is much lower than in the case of vertical flow. In this case, the validity of the adjacency relationship can be corrected based on the angle between the flow velocity vector at the boundary and the boundary normal. By traversing the boundary relationships of all sub-regions, a complete set of adjacent treatment sub-region pairs is obtained.

[0042] For each pair of adjacent purification sub-regions, the pollutant transport at their shared boundary needs to be quantitatively calculated. Several monitoring points are selected on the shared boundary to obtain the pollutant concentration and velocity vector at each point. The pollutant concentration gradient reflects the spatial rate of change of the concentration field and is obtained by calculating the ratio of the concentration difference between the monitoring points on both sides of the boundary to the spatial distance. The flow velocity vector contains information on velocity magnitude and flow direction, and is obtained using acoustic Doppler current metering or particle image velocimetry. Cross-boundary pollutant transport consists of two parts: convective transport and diffusion transport. Convective transport is determined by the product of the component of the velocity in the boundary normal and the pollutant concentration at that location, while diffusion transport depends on the product of the projection of the concentration gradient in the boundary normal and the diffusion coefficient. The shared boundary is discretized into several micro-segments, and the pollutant flux density at each micro-segment is calculated. The total pollutant mass exchange flux is obtained by integrating along the boundary. This flux is a vector, with its direction indicating the net direction of pollutant transport and its magnitude representing the transported mass per unit time. In cases where there are obvious flow channels at the boundary, the pollutant mass exchange flux is mainly determined by the flow rate and concentration of the channel, and it is necessary to focus on monitoring the velocity distribution and concentration distribution of the channel cross-section.

[0043] Establishing the coupling relationship of purification capabilities requires analyzing how adjustments to the purification intensity of a purification sub-region affect the pollution status of adjacent regions. The region providing pollutant input is called the source purification sub-region, and the region receiving the pollutant influence is called the target purification sub-region. Changes in the purification intensity of the source region alter the pollutant removal rate within that region, thus affecting the pollutant concentration and the mass of pollutants output from that region. When the purification intensity of the source region increases, the pollutant concentration within that region decreases, resulting in a reduction in the mass of pollutants output to the target region through the shared boundary, and consequently, a decrease in the pollution load of the target region. The strength of this effect depends on the magnitude of the change in the purification intensity of the source region and the pollutant exchange flux between the two regions. The purification capability coupling coefficient is defined as the change in the rate of change of the pollution load in the target region caused by a unit change in the purification intensity of the source region. The calculation of this coefficient requires considering the pollutant removal kinetics of the source region, the hydraulic residence time within the region, and the pollutant transport characteristics at the boundary. By applying a step-type purification intensity perturbation to the source region, monitoring the dynamic response curve of the pollution load in the target region, and fitting the initial slope of the response curve using a system identification method, the purification capability coupling coefficient is obtained. For complex regions with multiple pollutant transport paths, it is necessary to calculate the coupling coefficient of each path separately and then superimpose them.

[0044] By combining pollutant mass exchange flux and purification capacity coupling coefficient, the transmission mechanism of purification intensity changes between adjacent regions can be quantified. When the purification intensity of a certain treatment sub-region changes, this change first affects the pollutant concentration distribution in that region, and then is transmitted to adjacent regions through pollutant mass exchange flux, ultimately leading to changes in the pollution load of adjacent regions. The path of this transmission process is determined by the connection relationship between adjacent treatment sub-region pairs, and the transmission intensity is jointly determined by the purification capacity coupling coefficient and the pollutant mass exchange flux. For indirectly adjacent regions, although there is no direct boundary contact, the impact of purification intensity can be transmitted step by step through intermediate regions, forming a multi-hop transmission path. By tracing the complete path of pollutants from the source region through intermediate regions to the target region, the coupling coefficient of each segment is accumulated to obtain the indirect transmission intensity. Considering the time lag effect of pollutant transport in actual water bodies, the transmission intensity also needs to incorporate a time decay factor. The greater the distance between regions, the weaker the impact of purification intensity changes and the more significant the delay. Based on the coupling relationship of all adjacent treatment sub-region pairs, a correlation network between purification intensity and pollution load change rate is constructed.

[0045] The aforementioned relationships are organized in a standardized matrix format to facilitate subsequent numerical calculations and optimization solutions. A unique spatial index is assigned to each purification sub-region, with the index order arranged according to the region's spatial location, water flow direction, or pollution level. When constructing the matrix, row indices correspond to source purification sub-regions, column indices correspond to target purification sub-regions, and the values ​​of matrix elements represent the change in the pollution load rate of the target region caused by a unit change in the purification intensity of the source region. For adjacent region pairs with direct pollutant transport relationships, matrix elements are positive and relatively large; for region pairs without direct connections, matrix elements are zero or represent small positive values ​​indicating indirect transmission. The diagonal elements of the matrix reflect the influence of each purification sub-region's own purification intensity on the pollution load rate of its region, typically being negative, indicating that an increase in purification intensity leads to a decrease in the pollution load of the region. This cross-regional purification coordination matrix is ​​a sparse matrix, with non-zero elements mainly concentrated on the diagonal and its vicinity, reflecting the locality of pollutant transport.

[0046] In one optional implementation, the spatiotemporal field structure is decomposed into multi-scale components, and the pollutant concentration gradient field is decomposed into a background field component characterizing the macroscopic distribution trend and a perturbation field component characterizing the abrupt change characteristics of local pollutant concentration, including: Spatial spectrum analysis is performed on the pollutant concentration gradient field in the spatiotemporal field structure to transform the pollutant concentration gradient field from the spatial domain to the frequency domain, thereby obtaining the spatial spectrum distribution of the pollutant concentration gradient field. Calculate the energy proportion of each frequency component in the spatial spectrum distribution, and identify the cutoff frequency corresponding to the energy accumulation proportion in the spatial spectrum distribution reaching a preset energy accumulation threshold as the background field cutoff frequency. A spatial low-pass filter is constructed, and the cutoff frequency parameter of the spatial low-pass filter is set to the background field cutoff frequency. The spatial low-pass filter is used to perform spatial low-frequency filtering on the pollutant concentration gradient field, and low-frequency components with frequencies lower than the background field cutoff frequency in the pollutant concentration gradient field are extracted to obtain the background field component that characterizes the macroscopic distribution trend. Calculate the numerical difference between the pollutant concentration gradient field and the background field component at each spatial location point, extract the high-frequency component in the pollutant concentration gradient field whose frequency is higher than the cutoff frequency of the background field, and obtain the perturbation field component that characterizes the local pollutant concentration abrupt change.

[0047] After obtaining the spatiotemporal field structure describing the evolution of pollutant distribution, in-depth analysis of this structure is required to identify pollution characteristics at different scales. Fourier transform techniques are used to perform spatial spectral analysis on the pollutant concentration gradient field within the spatiotemporal field structure. This process transforms the pollutant concentration gradient field, originally defined in the spatial domain, into the frequency domain for characterization. Through two-dimensional discrete Fourier transform, the spatial coordinates are transformed... Pollutant concentration gradient values ​​at the location Convert to a spectral function in the frequency domain ,in and These represent spatial frequencies at... direction and The directional component. After transformation, the spatial spectral distribution of the pollutant concentration gradient field is obtained, which reflects the energy distribution of the pollutant concentration gradient field at different spatial frequency scales.

[0048] After obtaining the spatial spectrum distribution, the energy proportion of each frequency component in the frequency domain is calculated. Specifically, for each frequency point in the spatial spectrum distribution... The energy value of the frequency component is obtained by squaring the corresponding spectral amplitude. The total energy is obtained by summing the energy values ​​of all frequency components. The energy proportion distribution of each frequency component is obtained by calculating the ratio of the energy of each frequency component to the total energy. To identify the boundary between the background field and the disturbance field, the frequency components are sorted from low to high frequency, and the energy accumulation proportion curve is calculated. When the energy accumulation proportion reaches a preset energy accumulation threshold, the corresponding frequency is the background field cutoff frequency. The preset energy accumulation threshold is typically set between 0.85 and 0.95. This threshold is selected based on the statistical analysis results of a large amount of measured data on the distribution of pollutants in water bodies. Setting the energy accumulation threshold to 0.90 means that low-frequency components carry 90% of the energy in the pollutant concentration gradient field, and these low-frequency components represent the macroscopic distribution trend of pollutants.

[0049] After determining the background field cutoff frequency, a spatial low-pass filter is constructed to extract the background field components. The spatial low-pass filter is designed using a Butterworth filter structure, and its transfer function is... ,in This represents the frequency value of the current frequency component. The aforementioned background field cutoff frequency, This refers to the filter order. The value of is between 2 and 8. Higher order filters exhibit stronger frequency selectivity but introduce more pronounced edge oscillations. In practical applications, the filter order is typically chosen between 4 and 6 to balance frequency selectivity and filtering smoothness. The cutoff frequency parameter of the spatial low-pass filter is set to the identified background field cutoff frequency. Then, the spatial spectral distribution of the pollutant concentration gradient field is filtered in the frequency domain. The filtering process involves filtering the spatial spectral function. With filter transfer function Perform a dot product operation to obtain the filtered spectrum function. This filtering operation preserves low-frequency components below the background cutoff frequency while suppressing high-frequency components. The filtered spectrum function is then analyzed. Perform a two-dimensional inverse Fourier transform to convert it from the frequency domain back to the spatial domain, and obtain the background field component. This background field component reflects the macroscopic distribution trend of the pollutant concentration gradient field, exhibiting smooth and continuous spatial variation characteristics, and can characterize the large-scale transport and diffusion behavior of pollutants in water bodies.

[0050] After extracting the background field components, the perturbation field components are obtained through residual analysis. At each spatial location point... At this location, calculate the original pollutant concentration gradient field. With background field components The numerical difference between them, the formula for calculating the difference is: The difference field The disturbance field component, in its physical sense, represents the remaining local pollutant concentration gradient after removing the macroscopic distribution trend. This component contains abrupt changes in pollutant concentration, typically corresponding to rapid concentration variations caused by point source pollution emissions, localized water flow disturbances, and biochemical reaction hotspots. Spatially, the disturbance field component exhibits high-frequency oscillations; its numerical amplitude is relatively smaller than the background field component, but its gradient change rate is significantly higher. Spatial statistical analysis of the disturbance field component can identify regions where pollutant concentration gradients exceed specific thresholds, requiring rapid-response remediation measures.

[0051] After the multi-scale decomposition process described above, the original pollutant concentration gradient field is decomposed into two independent components: a background field component and a perturbation field component. These two components differ significantly in their spatial frequency characteristics, temporal evolution patterns, and response mechanisms to purification measures. The background field component exhibits a longer timescale and a wider spatial distribution, making it suitable for continuous and stable purification strategies. The perturbation field component, on the other hand, has a shorter timescale and a locally concentrated spatial distribution, requiring a rapid-response, pulse-type purification strategy. Separating these two types of field components provides a theoretical basis and data support for developing differentiated purification strategies targeting different pollution scales, significantly improving the accuracy and effectiveness of water treatment purification solutions.

[0052] In one optional implementation, determining the response weights of each purification sub-region to the background field component and the disturbance field component based on the cross-regional purification coordination matrix includes: Based on the magnitude of the coupling coefficient between the current purification sub-region and each adjacent purification sub-region, the set of adjacent purification sub-regions that affect the current purification sub-region is identified as the set of coupling influence source regions. Extract the background field component and disturbance field component of each coupled influence source region in the set of coupled influence source regions, calculate the spatial variation amplitude of pollutant concentration gradient value in the background field component as the background field variation amplitude, and calculate the spatial variation amplitude of pollutant concentration gradient value in the disturbance field component as the disturbance field variation amplitude. The sum of the background field change amplitude and the disturbance field change amplitude is calculated as the total change amplitude. The ratio of the background field change amplitude to the total change amplitude is calculated as the background field change contribution ratio. The ratio of the disturbance field change amplitude to the total change amplitude is calculated as the disturbance field change contribution ratio. The response weights of the current purification sub-region to the background field components are calculated based on the purification capacity coupling coefficient and the contribution ratio of the background field change. The response weights of the current purification sub-region to the disturbance field components are also calculated based on the purification capacity coupling coefficient and the contribution ratio of the disturbance field change.

[0053] When determining the response weights of each treatment sub-region to the background and disturbance field components, it is necessary to comprehensively consider the strength of the coupling relationship between regions and the contribution characteristics of different field components. For any current treatment sub-region in the water body to be treated, its purification strategy depends not only on its own pollution status but also on the pollution load transfer from neighboring treatment sub-regions. The cross-regional purification coordination matrix can be used to obtain the purification capacity coupling coefficients between the current treatment sub-region and all neighboring treatment sub-regions. These coupling coefficients reflect the strength of pollutant mass exchange between regions and the degree of interaction of purification capabilities.

[0054] To identify neighboring regions that truly have a substantial impact on the current treatment sub-region, a coupling strength threshold is set. This threshold can be calibrated based on the specific flow characteristics of the water body and historical monitoring data. The coupling coefficient of the purification capacity between the current treatment sub-region and each neighboring treatment sub-region is compared with this threshold. When the coupling coefficient of a neighboring treatment sub-region is greater than the threshold, it indicates that the neighboring region has a significant impact on the pollution load change of the current region. All neighboring treatment sub-regions that meet the criteria are summarized to form a set of coupling influence source regions. This screening mechanism avoids including the weak influence of all neighboring regions in the calculation, improving the accuracy and computational efficiency of response weight determination. For example, in the purification treatment of a river cross-section, the coupling coefficient of the upstream region is usually higher due to the direction of water flow, while the coupling coefficient of the lateral tributary confluence point depends on the ratio of tributary flow to main channel flow.

[0055] After obtaining the set of coupled influence source regions, it is necessary to extract the background field component and perturbation field component data for these regions. The background field component represents the macroscopic trend of pollutant concentration distribution, with relatively slow numerical changes and a relatively uniform spatial distribution; the perturbation field component captures abrupt changes in pollutant concentration in local areas, usually manifested as sharp spatial fluctuations. For each region in the set of coupled influence source regions, the pollutant concentration gradient value in its background field component is calculated using a spatial gradient operator. Specifically, on the spatial grid of the region, the ratio of the concentration difference between adjacent grid points to the spatial distance is calculated to obtain the concentration gradient vector at each location. Further calculation of the magnitude variation of these gradient vectors across the entire region can be performed using either the range method (the difference between the maximum and minimum gradient values ​​within the region) or the standard deviation method (calculating the dispersion of all gradient values ​​relative to the average gradient value). This variation magnitude serves as the background field variation magnitude, quantifying the spatial heterogeneity of the background field in the region.

[0056] The same method is applied to the analysis of perturbation field components. Since perturbation field components themselves characterize local abrupt changes, the spatial variation amplitude of their concentration gradient values ​​is usually significantly larger than that of the background field components. When calculating the concentration gradient of perturbation field components, a higher spatial resolution is needed to capture the details of their dramatic fluctuations. The amplitude of perturbation field variation reflects the degree of local anomaly in pollutant concentration within the region; a larger value indicates a more pronounced pollutant accumulation or rapid diffusion phenomenon in that region. For example, when an industrial wastewater discharge outlet is located in a coupled source region, the amplitude of perturbation field variation in that region will show a peak, while the amplitude of perturbation field variation in regions far from the pollution source will be relatively smaller.

[0057] To quantify the relative importance of the background field component and the perturbation field component to the current treatment sub-region, the sum of their variation amplitudes is calculated as the total variation amplitude. The total variation amplitude reflects the overall pollution dynamics of the coupled influence source region. The background field variation contribution ratio is obtained by calculating the ratio of the background field variation amplitude to the total variation amplitude; this ratio ranges from zero to one, with a value closer to one indicating that the pollution in the region is mainly dominated by macroscopic distribution trends. Correspondingly, the ratio of the perturbation field variation amplitude to the total variation amplitude is the perturbation field variation contribution ratio, reflecting the proportion of local abrupt changes in the pollution status of the region. The sum of the two contribution ratios is always equal to one, ensuring the completeness of the field component decomposition. In practical applications, for lake areas with gentle water flow and uniform pollution source distribution, the background field variation contribution ratio is usually higher; while for river sections with multiple point source emissions, the perturbation field variation contribution ratio dominates.

[0058] When determining the response weights, it is necessary to combine the coupling strength between regions with the contribution characteristics of the field components. For the response weight of the background field component, the coupling coefficient of the purification capacity between the current purification sub-region and each coupled source region is multiplied by the corresponding background field change contribution ratio to obtain the weight contribution value of each coupled source region to the current region's background field response. The weight contribution values ​​of all coupled source regions are normalized so that the sum of all background field response weights equals one, thus obtaining the response weight of the current purification sub-region to the background field component. This weight indicates which coupled source regions' macroscopic pollution loads need to be focused on when formulating a continuous basic purification strategy. Similarly, the purification capacity coupling coefficient is multiplied by the disturbance field change contribution ratio and normalized to obtain the response weight of the current purification sub-region to the disturbance field component. This weight guides the formulation of pulse-type response purification strategies, clarifying which regions need rapid responses to sudden local pollution events. Through this dual-weight mechanism, it is ensured that the purification strategy can address both slowly evolving macroscopic pollution trends and timely handle sudden local pollution events, achieving synergistic optimization of multi-scale purification objectives.

[0059] In one optional implementation, for the background field component and the disturbance field component, a continuous basic purification strategy and a pulsed response purification strategy are generated according to the response weights and then time-series superimposed to form a multi-timescale collaborative intelligent water treatment purification execution scheme, including: Based on the response weight of the current purification sub-region to the background field component, the purification load benchmark value corresponding to the background field component is calculated. The purification intensity parameter and purification duration parameter are determined according to the purification load benchmark value, and a continuous basic purification strategy is generated to purify the background field component with constant purification intensity in a continuous time period. Based on the response weights of the current purification sub-regions to the disturbance field components, the peak value of the purification load corresponding to the disturbance field components is calculated. The peak value of the purification load is used to determine the peak value of the purification and the peak value of the purification triggering time, and a pulse-type response purification strategy is generated to be activated at the moment of sudden change of the disturbance field components. A time-series purification intensity curve is constructed based on purification intensity parameters and purification duration parameters for a continuous basic purification strategy. Based on the peak purification intensity parameter and the purification trigger time parameter, a time-series purification intensity curve of the pulse-type response purification strategy is constructed. The time-series purification intensity curve of the continuous basic purification strategy is used as the baseline of the basic purification intensity, and the time-series purification intensity curve of the pulse-type response purification strategy is used as the purification intensity increment superimposed on the baseline of the basic purification intensity. By superimposing them on the time axis, a smart water treatment purification execution scheme with multi-time scale coordination is obtained.

[0060] like Figure 2 As shown, the method includes: For the generation process of a continuous basic purification strategy for the background field component, the response weight value of the current purification sub-region to the background field component is extracted. This response weight reflects the proportion of purification responsibility that the current sub-region should bear in the face of the macro-pollution distribution trend. Based on the response weight and the average pollutant concentration value of the sub-region in the background field component, the purification load benchmark value corresponding to the background field component is calculated. The purification load benchmark value represents the mass of pollutants that need to be continuously removed per unit time, and its value is equal to the product of the background field component concentration and the response weight multiplied by the water volume of the sub-region. Based on the purification load benchmark value and combined with the treatment efficiency characteristic curve of the purification equipment, the purification intensity parameter that can meet the continuous removal requirements is determined. The purification intensity parameter is usually expressed as the treatment flow rate of the purification equipment or the reagent dosing rate per unit time. At the same time, based on the continuous existence characteristic of the background field component, the purification duration parameter is set to cover the time span of the entire monitoring period. Based on the above purification intensity parameter and purification duration parameter, a continuous basic purification strategy for purifying the background field component with a constant purification intensity over a continuous time period is generated. This strategy ensures that the purification sub-region can cope with the continuous input of macro-pollution load with stable treatment capacity.

[0061] The process of generating a pulse-type response purification strategy for the disturbance field component involves extracting the response weight value of the current purification sub-region to the disturbance field component. This response weight reflects the priority level of the current sub-region when facing localized sudden pollution. Based on the response weight and the peak concentration of the maximum pollutant in the disturbance field component for that sub-region, the peak purification load corresponding to the disturbance field component is calculated. The peak purification load represents the maximum mass of pollutant that needs to be rapidly removed at the moment of abrupt change; its value is equal to the product of the peak concentration of the disturbance field component and the response weight, multiplied by the volume of the affected water body. Based on the peak purification load, combined with the maximum treatment capacity and response time characteristics of the purification equipment, the peak purification intensity parameter is determined. The peak purification intensity parameter is set as the upper limit of the treatment capacity that can rapidly reduce localized high-concentration pollution within a short period of time. Simultaneously, by monitoring the time-series change trend of the disturbance field component, the moment when the concentration gradient rises sharply is identified, and this moment is determined as the purification trigger moment parameter. The purification trigger moment parameter is typically set at the moment when the concentration of the disturbance field component exceeds a preset multiple threshold of the background field component concentration. Based on the above peak purification intensity parameter and purification trigger moment parameter, a pulse-type response purification strategy is generated that is activated at the moment of abrupt change in the disturbance field component. This strategy ensures that the purification sub-areas can respond quickly and effectively to sudden pollution incidents.

[0062] After obtaining the parameters for the two types of purification strategies, corresponding time-series purification intensity curves need to be constructed for subsequent superposition operations. For the continuous basic purification strategy, its time-series purification intensity curve is constructed based on the purification intensity parameter and the purification duration parameter. This curve presents a rectangular wave shape with a constant value across the entire time axis, its amplitude equal to the purification intensity parameter value, and the time span extending from the start to the end of the monitoring period. For the pulse-type response purification strategy, its time-series purification intensity curve is constructed based on the purification peak intensity parameter and the purification trigger time parameter. This curve presents multiple pulse wave shapes, with the peak intensity of each pulse wave equal to the purification peak intensity parameter value. The start time of the pulse wave corresponds to the purification trigger time parameter, and the decay process of the pulse wave is dynamically adjusted according to the decreasing trend of the perturbation field component concentration. An exponential decay function is typically used to describe the time evolution of the pulse purification intensity, ensuring that the purification intensity decreases accordingly as the perturbation field component concentration gradually decreases.

[0063] After constructing the two types of time-series purification intensity curves, a time-series superposition operation is performed to form the final intelligent water treatment purification execution scheme. Specifically, during the superposition process, the time-series purification intensity curve of the continuous basic purification strategy is used as the baseline purification intensity, representing the normalized purification needs to address macroscopic pollution distribution. The time-series purification intensity curve of the impulse-response purification strategy is used as the purification intensity increment superimposed on the baseline purification intensity, representing the additional purification needs to address sudden pollution events. Numerical superposition is performed on the time axis. For any given moment, the final purification intensity equals the sum of the purification intensity values ​​of the continuous basic purification strategy and the impulse-response purification strategy at that moment. When there are no abrupt changes in the perturbation field components, the final purification intensity equals the baseline purification intensity; when abrupt changes occur in the perturbation field components, the final purification intensity will be superimposed on the baseline purification intensity, resulting in a temporary increase in purification capacity. Through the above time-series superposition operation, a multi-timescale intelligent water treatment and purification execution scheme that simultaneously includes long-term steady-state purification and short-term pulse purification is obtained. This scheme can take into account both the stable removal of continuous pollution loads and the rapid response to sudden pollution events.

[0064] A second aspect of the present invention provides an intelligent water treatment and purification system for environmental engineering, comprising: The monitoring and analysis unit is used to obtain the spatiotemporal field structure describing the evolution of pollutant distribution by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field based on real-time monitoring data of the water body to be treated. The region division unit is used to divide the water body to be treated into multiple purification treatment sub-regions based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel in the spatiotemporal field structure. The collaborative matrix unit is used to establish the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, and to obtain the cross-regional purification collaborative matrix. Multi-scale sub-units are used to decompose the spatiotemporal field structure into multi-scale components, decomposing the pollutant concentration gradient field into background field components characterizing the macroscopic distribution trend and perturbation field components characterizing the abrupt change characteristics of local pollutant concentration. The weight determination unit is used to determine the response weights of each purification sub-region to the background field component and the disturbance field component based on the cross-regional purification coordination matrix. The strategy generation unit is used to generate a continuous basic purification strategy and a pulse response purification strategy for the background field component and the disturbance field component according to the response weight, and then superimpose them in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

[0065] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0066] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0067] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent water treatment and purification method for environmental protection projects, characterized in that, include: Based on real-time monitoring data of the water body to be treated, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field. Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification treatment sub-regions; by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification treatment sub-regions, the correlation between the purification intensity of each purification treatment sub-region and the pollution load change rate of adjacent purification treatment sub-regions is established, and a cross-regional purification synergy matrix is ​​obtained. The spatiotemporal field structure is decomposed into multi-scale components, and the pollutant concentration gradient field is decomposed into background field components that characterize the macroscopic distribution trend and perturbation field components that characterize the abrupt change characteristics of local pollutant concentration. Based on the cross-regional purification collaboration matrix, the response weights of each purification sub-region to the background field component and the disturbance field component are determined. For the background field component and the disturbance field component, a continuous basic purification strategy and an impulse response purification strategy are generated according to the response weights and then superimposed in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

2. The method according to claim 1, characterized in that, Based on real-time monitoring data of the water body to be treated, by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field, the spatiotemporal field structure describing the evolution of pollutant distribution is obtained, including: Based on the pollutant concentration distribution data and water flow characteristic data in the real-time monitoring data, the pollutant concentration gradient vector at each spatial location in the water body to be treated is calculated to obtain the pollutant concentration gradient field. The flow velocity vector and flow direction information at each spatial location in the water body to be treated are extracted to obtain the flow velocity field. By performing a vector synthesis operation on the spatially corresponding pollutant concentration gradient vector and the flow velocity vector, the comprehensive transport vector field of pollutants under the dual effects of concentration gradient driving and flow velocity carrying is calculated, and the pollutant transport rate distribution and pollutant transport direction distribution are obtained. Based on the pollutant transport rate distribution, by calculating the rate of change of concentration gradient between adjacent spatial locations in the pollutant concentration gradient field, the spatial boundary where the rate of change of concentration gradient exceeds the preset gradient abruptness judgment threshold is identified, and the location of the concentration gradient abruptness boundary is obtained. Based on the pollutant transport direction distribution, by tracing the continuous distribution path of the flow velocity vector in space, spatial channel regions with consistent flow direction and flow rate higher than a preset flow intensity threshold are identified, thereby obtaining the flow direction characteristics of the pollutant transport channel. By spatially superimposing the pollutant concentration gradient field, the flow velocity field, the location of the concentration gradient abrupt change boundary, and the flow direction characteristics of the pollutant transport channel, a spatiotemporal field structure describing the evolution of pollutant distribution is obtained.

3. The method according to claim 1, characterized in that, Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channels in the spatiotemporal field structure, the water body to be treated is divided into multiple purification sub-regions, including: Based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel, the angle between the flow direction vector of the pollutant transport channel and the normal vector of the concentration gradient abrupt change boundary location at each concentration gradient abrupt change boundary location is calculated to obtain the flow crossing angle distribution. Based on the flow crossing angle distribution, the location of the concentration gradient abrupt change boundary where the flow crossing angle is less than the preset crossing judgment angle threshold is identified as the active crossing boundary of the pollutant, and the location of the concentration gradient abrupt change boundary where the flow crossing angle is greater than the preset crossing judgment angle threshold is identified as the pollutant blocking boundary. Based on the active crossing boundary and the barrier boundary of the pollutants, a pollutant transmission impedance field is constructed for the water body to be treated. By calculating the shortest transmission impedance distance from each spatial location in the pollutant transmission impedance field to the barrier boundary of the pollutants, the impedance distance distribution of each spatial location is obtained. According to the impedance distance distribution, spatial locations where the difference of the shortest transmission impedance distance is less than a preset distance clustering threshold are clustered into the same purification treatment sub-region.

4. The method according to claim 1, characterized in that, By quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions is established, resulting in a cross-regional purification synergy matrix, which includes: Based on the spatial adjacency relationship of the multiple purification sub-regions, purification sub-regions that have spatial boundary contact are regarded as adjacent purification sub-region pairs. Based on the pollutant concentration gradient and flow velocity vector at the shared boundary between the adjacent purification sub-regions, the pollutant mass transported across the boundary per unit time is calculated as the pollutant mass exchange flux between the adjacent purification sub-regions. Based on the pollutant mass exchange flux, the influence coefficient of the change in purification intensity of the source purification treatment sub-region on the pollution load change rate of the target purification treatment sub-region is calculated as the purification capacity coupling coefficient. Based on the pollutant mass exchange flux and the purification capacity coupling coefficient, the transmission path and transmission intensity of the change in purification intensity of the purification treatment sub-region on the pollution load change rate of adjacent purification treatment sub-regions are quantified, and the correlation between purification intensity and pollution load change rate is obtained. The correlation between the purification intensity and the pollution load change rate of all purification sub-regions is arranged in matrix form according to the spatial index order of the purification sub-regions to obtain the cross-regional purification coordination matrix.

5. The method according to claim 1, characterized in that, The spatiotemporal field structure is decomposed into multiple scales, and the pollutant concentration gradient field is decomposed into a background field component characterizing the macroscopic distribution trend and a perturbation field component characterizing the abrupt change characteristics of local pollutant concentration, including: Spatial spectrum analysis is performed on the pollutant concentration gradient field in the spatiotemporal field structure to transform the pollutant concentration gradient field from the spatial domain to the frequency domain, thereby obtaining the spatial spectrum distribution of the pollutant concentration gradient field. Calculate the energy proportion of each frequency component in the spatial spectrum distribution, and identify the cutoff frequency corresponding to the energy accumulation proportion in the spatial spectrum distribution reaching a preset energy accumulation threshold as the background field cutoff frequency. A spatial low-pass filter is constructed, and the cutoff frequency parameter of the spatial low-pass filter is set to the background field cutoff frequency. The spatial low-pass filter is used to perform spatial low-frequency filtering on the pollutant concentration gradient field, and low-frequency components with frequencies lower than the background field cutoff frequency in the pollutant concentration gradient field are extracted to obtain the background field component that characterizes the macroscopic distribution trend. Calculate the numerical difference between the pollutant concentration gradient field and the background field component at each spatial location point, extract the high-frequency component in the pollutant concentration gradient field whose frequency is higher than the cutoff frequency of the background field, and obtain the perturbation field component that characterizes the local pollutant concentration abrupt change.

6. The method according to claim 1, characterized in that, Based on the cross-regional purification coordination matrix, the response weights of each purification sub-region to the background field component and the disturbance field component are determined as follows: Based on the magnitude of the coupling coefficient between the current purification sub-region and each adjacent purification sub-region, the set of adjacent purification sub-regions that affect the current purification sub-region is identified as the set of coupling influence source regions. Extract the background field component and disturbance field component of each coupled influence source region in the set of coupled influence source regions, calculate the spatial variation amplitude of pollutant concentration gradient value in the background field component as the background field variation amplitude, and calculate the spatial variation amplitude of pollutant concentration gradient value in the disturbance field component as the disturbance field variation amplitude. The sum of the background field change amplitude and the disturbance field change amplitude is calculated as the total change amplitude. The ratio of the background field change amplitude to the total change amplitude is calculated as the background field change contribution ratio. The ratio of the disturbance field change amplitude to the total change amplitude is calculated as the disturbance field change contribution ratio. The response weights of the current purification sub-region to the background field components are calculated based on the purification capacity coupling coefficient and the contribution ratio of the background field change. The response weights of the current purification sub-region to the disturbance field components are also calculated based on the purification capacity coupling coefficient and the contribution ratio of the disturbance field change.

7. The method according to claim 1, characterized in that, For the background field component and the disturbance field component, a continuous basic purification strategy and an impulse response purification strategy are generated according to the response weights and then superimposed in time series to form a multi-timescale collaborative intelligent water treatment purification execution scheme, including: Based on the response weight of the current purification sub-region to the background field component, the purification load benchmark value corresponding to the background field component is calculated. The purification intensity parameter and purification duration parameter are determined according to the purification load benchmark value, and a continuous basic purification strategy is generated to purify the background field component with constant purification intensity in a continuous time period. Based on the response weights of the current purification sub-regions to the disturbance field components, the peak value of the purification load corresponding to the disturbance field components is calculated. The peak value of the purification load is used to determine the peak value of the purification and the peak value of the purification triggering time, and a pulse-type response purification strategy is generated to be activated at the moment of sudden change of the disturbance field components. The time-series purification intensity curve of the continuous basic purification strategy is constructed based on the purification intensity parameter and the purification duration parameter; the time-series purification intensity curve of the pulse response purification strategy is constructed based on the purification peak intensity parameter and the purification trigger time parameter. The time-series purification intensity curve of the continuous basic purification strategy is used as the baseline of the basic purification intensity, and the time-series purification intensity curve of the pulse-type response purification strategy is used as the purification intensity increment superimposed on the baseline of the basic purification intensity. By superimposing them on the time axis, a smart water treatment purification execution scheme with multi-time scale coordination is obtained.

8. An intelligent water treatment and purification system for environmental protection projects, used to implement the method as described in any one of claims 1-7, characterized in that, include: The monitoring and analysis unit is used to obtain the spatiotemporal field structure describing the evolution of pollutant distribution by establishing the coupling relationship between the pollutant concentration gradient field and the flow velocity field based on real-time monitoring data of the water body to be treated. The region division unit is used to divide the water body to be treated into multiple purification treatment sub-regions based on the location of the concentration gradient abrupt change boundary and the flow direction characteristics of the pollutant transport channel in the spatiotemporal field structure. The collaborative matrix unit is used to establish the correlation between the purification intensity of each purification sub-region and the pollution load change rate of adjacent purification sub-regions by quantifying the pollutant mass exchange flux and purification capacity coupling coefficient between adjacent purification sub-regions, and to obtain the cross-regional purification collaborative matrix. Multi-scale sub-units are used to decompose the spatiotemporal field structure into multi-scale components, decomposing the pollutant concentration gradient field into background field components characterizing the macroscopic distribution trend and perturbation field components characterizing the abrupt change characteristics of local pollutant concentration. The weight determination unit is used to determine the response weights of each purification sub-region to the background field component and the disturbance field component based on the cross-regional purification coordination matrix. The strategy generation unit is used to generate a continuous basic purification strategy and a pulse response purification strategy for the background field component and the disturbance field component according to the response weight, and then superimpose them in time to form a multi-timescale collaborative intelligent water treatment purification execution scheme.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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