Low energy consumption wastewater treatment method and system

Abstract

The present application relates to the technical field of wastewater treatment, and discloses a low-energy wastewater treatment method and system.The method comprises the following steps: performing multi-stage cyclone separation on target wastewater to be treated to obtain gradientized distribution of the target wastewater to be treated; performing spatiotemporal correlation analysis on the load distribution of the gradientized distribution to obtain distribution characteristics of the gradientized distribution; performing energy self-consistent conversion on the gradientized distribution based on the distribution characteristics to obtain a self-organizing micro-turbulent system of the gradientized distribution; performing nonlinear shear modulation on the self-organizing micro-turbulent system to obtain intelligent migration flocculation bodies of the self-organizing micro-turbulent system; performing multi-modal parameter collaborative coding on the intelligent migration flocculation bodies to obtain process control instructions of the intelligent migration flocculation bodies; and performing accurate separation of phase interfaces on the intelligent migration flocculation bodies based on the process control instructions to obtain purified effluent of the target wastewater to be treated.The present application can improve the efficiency of low-energy wastewater treatment.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a low-energy wastewater treatment method and system. Background Technology

[0002] In existing wastewater treatment technologies, especially mainstream processes based on activated sludge, advanced oxidation, or membrane separation, energy consumption is primarily concentrated in exogenous mechanical aeration, stirring, or pressurization to achieve thorough mixing, mass transfer, and reaction. These processes rely on a continuous input of high-grade electrical energy to maintain fluid turbulence or transmembrane pressure differential, resulting in persistently high overall system energy consumption. Furthermore, traditional methods are slow to respond to dynamic changes in influent load, often employing fixed operating parameters or delayed feedback regulation. This fails to achieve a precise match between energy input and real-time treatment demands, leading to significant energy wastage through unnecessary excessive stirring or aeration.

[0003] Existing technologies have inherent limitations in enhancing interphase mass transfer and selective removal of target pollutants. Conventional flocculation processes rely on random collision mechanisms, resulting in loosely structured flocs with low density, leading to increased subsequent settling or separation loads and low reagent utilization efficiency. For complex and variable wastewater systems, the lack of real-time perception and precise control over the floc formation process and its interfacial characteristics makes the separation process reliant on empirical operation, resulting in unclear solid-liquid separation interfaces, insufficient effluent quality stability, and difficulty in systematically reducing reagent consumption and separation energy consumption while ensuring treatment efficiency. Therefore, improving the treatment efficiency of low-energy wastewater has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a low-energy wastewater treatment method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a low-energy wastewater treatment method, comprising: S01. Perform multi-stage cyclone separation on the target wastewater to be treated to obtain a gradient diversion of the target wastewater to be treated; S02. Perform spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting; S03. Based on the distribution characteristics, perform energy self-consistent transformation on the gradient split to obtain the self-organized microturbulent system of the gradient split; S04. The self-organized microturbulent system is subjected to nonlinear shear modulation to obtain the intelligent migration flocculants of the self-organized microturbulent system; S05. Perform multimodal parameter co-coding on the intelligent migration flocculant to obtain the process control command for the intelligent migration flocculant; S06. Based on the process control instructions, the intelligent migration flocculants are precisely separated at the phase interface to obtain the purified effluent of the target wastewater to be treated.

[0006] In a preferred embodiment, the step of performing multi-stage cyclone separation on the target wastewater to obtain a gradient diversion of the target wastewater includes: The original flow regime characteristics of the target wastewater to be treated are identified to obtain the fluid dynamic fingerprint of the target wastewater to be treated; Based on the fluid dynamic fingerprint, the target wastewater to be treated is structured into layers, and the layered wastewater is subjected to continuous phase centrifugal separation to obtain the intermediate separated flow of the target wastewater to be treated. The intermediate separated flow is subjected to vortex core-edge co-precipitation to obtain the gradient separation of the target wastewater to be treated.

[0007] In a preferred embodiment, the step of performing spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting includes: The gradient-based traffic splitting is continuously tracked and located to obtain the load evolution segments of the gradient-based traffic splitting; Based on the load evolution segment, the path dependence of the pollution load in the gradient diversion is identified to obtain the load transfer trajectory of the gradient diversion. Spatial hub analysis is performed on the interaction of the load transfer trajectories to obtain the aggregation nodes and diversion nodes of the pollution load; Based on the aggregation node and the splitting node, multimodal feature integration is performed on the gradient splitting to obtain the distribution characteristics of the gradient splitting.

[0008] In a preferred embodiment, the step of performing energy self-consistent transformation on the gradient-split flow based on the distribution characteristics to obtain the self-organized microturbulent system of the gradient-split flow includes: The pressure field and velocity field of the gradient diversion are simultaneously mapped to obtain a gradient distribution cloud map of the real-time hydraulic potential energy in the gradient diversion. Based on the high potential energy continuous region of the gradient distribution cloud map, the streamline of the gradient split is reshaped to obtain the guiding fluid of the gradient split; Kinetic energy enrichment is performed on the guiding fluid to obtain a shear jet of the guiding fluid; The shear layer of the shear jet is excited at a subharmonic frequency to obtain a phase-synchronous vortex ring sequence of the shear jet; The geometric boundary of the local flow channel where the phase-synchronous vortex ring sequence is located is modulated and reconstructed to obtain the self-organized microturbulent system of gradient-split flow.

[0009] In a preferred embodiment, the subharmonic frequency excitation of the shear layer of the shear jet to obtain the phase-synchronized vortex ring sequence of the shear jet includes: The flow field characteristics of the shear jet are simultaneously acquired using multiple parameters to obtain the multi-scale flow characteristic parameters of the shear jet; Based on the multi-scale flow characteristic parameters, the optimal subharmonic excitation frequency of the shear layer in the shear jet is calculated, wherein the formula for calculating the optimal subharmonic excitation frequency is: ; In the formula, The optimal subharmonic excitation frequency is... The dominant frequency in the multi-scale flow characteristic parameters is... The convection velocity of the shear layer in the multi-scale flow characteristic parameters is... The root mean square velocity of the flow pulsation velocity in the shear layer is one of the multi-scale flow characteristic parameters. The dominant frequency in the multi-scale flow characteristic parameters is the turbulent kinetic energy spectral density value. This refers to the turbulent kinetic energy spectral density value at half the dominant frequency in the multi-scale flow characteristic parameters. The natural logarithm operator. The Reynolds number is the characteristic parameter of the multi-scale flow. The preset critical Reynolds number, Pi is a constant. is the base of the natural logarithm; Based on the optimal subharmonic excitation frequency, the shear layer is periodically perturbed and embedded to obtain the shear layer waveform of the shear jet; Based on the shear layer waveform, the shear jet is self-organized and coiled to obtain the phase-synchronized vortex ring sequence of the shear jet.

[0010] In a preferred embodiment, the nonlinear shear modulation of the self-organized microturbulent system to obtain intelligent migrating flocs of the self-organized microturbulent system includes: The rotation direction and angular velocity of the self-organized microturbulent system are identified by multiple parameters to obtain the vortex dynamics parameter set of the self-organized microturbulent system; Based on the vortex dynamics parameter set, the tangential velocity boundary layer of adjacent vortices in the self-organized microturbulent system is enhanced by converging, resulting in a high-intensity shear band in the self-organized microturbulent system. The fine particles suspended within the high-intensity shear band are driven by inertial force gradient to obtain the migrating particle group of the self-organized microturbulent system. The migrating particle group is polymer chain entangled to obtain the intelligent migrating flocculent of the self-organized microturbulent system.

[0011] In a preferred embodiment, the step of performing multimodal parameter co-coding on the intelligent migration flocs to obtain process control instructions for the intelligent migration flocs includes: Simultaneous imaging of the spatial distribution density and surface potential distribution of the intelligent migrating flocs yields a dual-modal spatial distribution spectrum of the intelligent migrating flocs. Based on the dual-modal spatial distribution spectrum, the structural stability of the intelligent migratory flocs is divided by structural gradient to obtain the stability gradient of the intelligent migratory flocs. Based on the stability gradient, the focal position and intensity of energy application in the smart migration floc are reverse-engineered, and the derived information is used for strategy mapping to obtain the targeted energy application scheme of the smart migration floc. The targeted energy application scheme is compiled into an instruction sequence to obtain the process control instructions for the intelligent migration flocculant.

[0012] In a preferred embodiment, the step of back-deriving the focal position and intensity of energy application in the smart migration floc based on the stability gradient, and then performing strategy mapping on the derived information to obtain a targeted energy application scheme for the smart migration floc, includes: Based on the stability gradient, the baseline energy intensity value of the intelligent migrating flocculant is calculated, wherein the formula for calculating the baseline energy intensity value is: ; In the formula, The reference energy intensity value, The preset process constants for the intelligent migration flocculants, This is the maximum value of the stability gradient. The Gaussian error function is... Let be the mean of the stability gradient. Let be the standard deviation of the stability gradient. It is an exponential function. This is a preset value to prevent the division of small positive numbers into zero; By performing key point traversal detection on the spatial isosurface of the stability gradient, a candidate focal coordinate set of the intelligent migrating flocs is obtained. Based on the baseline energy intensity value and the candidate focus coordinate set, the smart migration flocs are spatiotemporally arranged to obtain a targeted energy application scheme for the smart migration flocs.

[0013] In a preferred embodiment, the step of precisely separating the phase interface of the intelligent migrating flocs based on the process control instructions to obtain the purified effluent of the target wastewater includes: Based on the process control command, focused energy injection is performed on the intelligent migration flocculants to obtain the dispersed flocculants of the self-organized microturbulent system; Hydraulic stratification was induced in the dispersed flocs to obtain the solid-liquid separation state of the self-organized microturbulent system; Based on the solid-liquid separation state, the aqueous phase of the self-organized microturbulent system is clarified and guided to obtain the purified effluent of the target wastewater to be treated.

[0014] To address the above problems, the present invention also provides a low-energy wastewater treatment system, the system comprising: The gradient diversion pretreatment module is used to perform multi-stage cyclone separation on the target wastewater to obtain the gradient diversion of the target wastewater; The load spatiotemporal feature analysis module is used to perform spatiotemporal correlation analysis on the load distribution of the gradient-based load splitting to obtain the distribution features of the gradient-based load splitting. An energy self-consistent turbulence generation module is used to perform energy self-consistent transformation on the gradient split based on the distribution characteristics, so as to obtain the self-organized microturbulent system of the gradient split; The intelligent floc modulation module is used to perform nonlinear shear modulation on the self-organized microturbulent system to obtain intelligent migration flocs of the self-organized microturbulent system; An adaptive decision control module is used to perform multimodal parameter co-encoding on the intelligent migration flocculant to obtain process control instructions for the intelligent migration flocculant. The phase interface precision separation module is used to perform precise phase interface separation on the intelligent migration flocs based on the process control instructions, so as to obtain the purified effluent of the target wastewater to be treated.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves gradient diversion of wastewater through multi-stage cyclone separation, and accurately obtains the pollution load distribution characteristics by combining spatiotemporal correlation analysis. Based on these characteristics, energy self-consistent conversion is carried out to form a stable self-organized microturbulent system, which can significantly enhance the migration and aggregation efficiency of pollutants, improve the regularity and stability of floc formation, ensure the efficient connection of subsequent treatment links, and greatly improve the continuity and treatment effect of the overall wastewater treatment process.

[0016] 2. This invention achieves precise control over the floc formation process through nonlinear shear modulation and multimodal parameter co-coding, generating highly adaptable process control commands to drive precise separation at the phase interface, effectively improving the solid-liquid separation accuracy, reducing pollutant residue, ensuring the stability and compliance rate of purified water quality, and optimizing the energy utilization efficiency of the treatment process, thereby improving the operational reliability and adaptability of the treatment system. Attached Figure Description

[0017] Figure 1 A schematic flowchart of a low-energy wastewater treatment method provided in an embodiment of the present invention; Figure 2 A functional block diagram of a low-energy wastewater treatment system provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a low-energy wastewater treatment method. The executing entity of this low-energy wastewater treatment method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the low-energy wastewater treatment method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a schematic flow chart of a low-energy wastewater treatment method according to an embodiment of the present invention. In this embodiment, the low-energy wastewater treatment method includes: S01. Perform multi-stage cyclone separation on the target wastewater to be treated to obtain a gradient diversion of the target wastewater to be treated; In this embodiment of the invention, the step of performing multi-stage cyclone separation on the target wastewater to obtain a gradient diversion of the target wastewater includes: The original flow regime characteristics of the target wastewater to be treated are identified to obtain the fluid dynamic fingerprint of the target wastewater to be treated; Based on the fluid dynamic fingerprint, the target wastewater to be treated is structured into layers, and the layered wastewater is subjected to continuous phase centrifugal separation to obtain the intermediate separated flow of the target wastewater to be treated. The intermediate separated flow is subjected to vortex core-edge co-precipitation to obtain the gradient separation of the target wastewater to be treated.

[0021] When identifying the original flow characteristics of the target wastewater, a flow field detection device is used to comprehensively collect the flow state of the wastewater. The probes of the detection device are evenly arranged at different cross-sections and depths of the wastewater conveying channel to continuously capture the flow state information such as the velocity distribution, flow direction changes and turbulent fluctuations of the wastewater during natural flow. By integrating and sorting out these collected flow state information, a fluid dynamic fingerprint that can uniquely characterize the flow characteristics of the batch of wastewater to be treated is formed.

[0022] Based on the obtained fluid dynamic fingerprint, according to the differences in the flow characteristics of each component of the wastewater reflected by the fingerprint, a layered component with a specific flow guiding structure is set in the wastewater treatment channel. The flow guiding structure is designed according to the flow trajectory of different components in the fluid dynamic fingerprint, guiding the wastewater to naturally form multiple parallel flow layers with different component concentrations during the flow process. After the structured layering is completed, the layered wastewater is continuously sent to a centrifugal separation device. The rotating mechanism inside the device drives the wastewater to perform high-speed circular motion. By utilizing the centrifugal force difference generated by the density difference between different components, the impurity components in each layer of wastewater are initially separated from the aqueous phase, and finally the intermediate separated flow is obtained.

[0023] The intermediate separated flow is introduced into a dedicated vortex generator, which contains a vortex guide and an edge precipitation component. The vortex guide leads the intermediate separated flow to form a stable vortex flow field, while the edge precipitation component is positioned to fit the edge region of the vortex flow field. By changing the cross-sectional size of the flow channel in the edge region, the flow velocity is adjusted, allowing the components at the edge of the vortex to precipitate smoothly. At the same time, the components in the core region of the vortex gather and gradually precipitate due to their centripetal force. Through this synergistic effect between the vortex core and the edge, different components in the intermediate separated flow are precipitated sequentially according to their concentration gradient, ultimately resulting in a gradient separation of the target wastewater.

[0024] The beneficial effects are that by relying on the original flow characteristics, the flow characteristics of wastewater can be accurately captured, forming a fluid dynamic fingerprint that can characterize the characteristics of the wastewater itself. Based on this fingerprint, the structured stratification operation can realize the orderly stratification of wastewater according to its flow characteristics. Combined with the continuous phase centrifugal separation, the phase separation of each layer of wastewater is completed, resulting in a clearly defined intermediate separated flow. With the help of the vortex core edge synergistic precipitation operation, the pressure difference and velocity gradient generated by the vortex flow are used to achieve the directional precipitation of pollutants. The final gradient diversion has clear component differences, which can provide a precise diversion basis for subsequent wastewater treatment, and improve the targeting and effectiveness of the overall treatment process.

[0025] S02. Perform spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting; In this embodiment of the invention, the step of performing spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting includes: The gradient-based traffic splitting is continuously tracked and located to obtain the load evolution segments of the gradient-based traffic splitting; Based on the load evolution segment, the path dependence of the pollution load in the gradient diversion is identified to obtain the load transfer trajectory of the gradient diversion. Spatial hub analysis is performed on the interaction of the load transfer trajectories to obtain the aggregation nodes and diversion nodes of the pollution load; Based on the aggregation node and the splitting node, multimodal feature integration is performed on the gradient splitting to obtain the distribution characteristics of the gradient splitting.

[0026] When continuously tracking and locating gradient diversion, distributed sensing components are deployed along the entire diversion transport path. The detection points of the sensing components uniformly cover each flow layer and key flow area of ​​the diversion, continuously capturing the real-time changes of pollution load at each point. At the same time, the time information and spatial location information corresponding to each detection point are recorded. Pollution load data at different times at the same spatial location are concatenated in chronological order, and then the concatenated data from different spatial locations are correlated and integrated to form a load evolution segment that can reflect the process of pollution load changing with time and space.

[0027] Based on the obtained load evolution segments, the distribution of pollution load at different time points in the segments is extracted. Combined with the flow direction of the diversion, the movement path of the pollution load from the initial detection location to subsequent detection locations is sorted out. By comparing the differences in the movement path of the pollution load in different time periods, the dependence of the pollution load on the previous path during the movement process is identified, that is, the influence of the previous flow trajectory on the subsequent movement path. According to this dependency, the paths are connected in chronological order to form a complete gradient diversion load transfer trajectory.

[0028] The obtained load transfer trajectories are observed throughout the entire process, and the intersection and branching of multiple trajectories are tracked. When multiple load transfer trajectories converge at the same spatial location, that spatial location is the pollution load convergence node. When one or more load transfer trajectories extend in different directions from the same spatial location, that spatial location is the pollution load diversion node. By examining the interaction of all load transfer trajectories segment by segment, all convergence nodes and diversion nodes are fully identified.

[0029] Based on the identified convergence and divergence nodes, we collect various characteristic information on the gradient divergence, including pollution load concentration changes, flow velocity changes, and load residence time characteristics in these nodes and surrounding areas. We then systematically integrate these different dimensions of characteristic information, eliminate duplicate information, and retain the core characteristics that reflect the overall load distribution pattern of the gradient divergence, ultimately forming the distribution characteristics of the gradient divergence.

[0030] The beneficial effects are as follows: by continuously tracking and locating the gradient diversion, the dynamic change process of the pollution load can be fully captured and the load evolution segment can be formed. Based on the path dependence identification of the pollution load based on the load evolution segment, the transmission sequence and change law of the pollution load can be clearly sorted out and the load transmission trajectory can be obtained. The spatial hub analysis of the interaction of the load transmission trajectory can accurately locate the aggregation node and diversion node of the pollution load. Based on the multimodal feature integration of these nodes, the dispersed load characteristics can be integrated into a unified distribution characteristic. This distribution characteristic can comprehensively reflect the spatiotemporal distribution law of the gradient diversion load, and continuously provide reliable feature basis for the precise implementation of subsequent wastewater treatment.

[0031] S03. Based on the distribution characteristics, perform energy self-consistent transformation on the gradient split to obtain the self-organized microturbulent system of the gradient split; In this embodiment of the invention, the step of performing energy self-consistent transformation on the gradient-based flow based on the distribution characteristics to obtain the self-organized microturbulent system of the gradient-based flow includes: The pressure field and velocity field of the gradient diversion are simultaneously mapped to obtain a gradient distribution cloud map of the real-time hydraulic potential energy in the gradient diversion. Based on the high potential energy continuous region of the gradient distribution cloud map, the streamline of the gradient split is reshaped to obtain the guiding fluid of the gradient split; Kinetic energy enrichment is performed on the guiding fluid to obtain a shear jet of the guiding fluid; The shear layer of the shear jet is excited at a subharmonic frequency to obtain a phase-synchronous vortex ring sequence of the shear jet; The geometric boundary of the local flow channel where the phase-synchronous vortex ring sequence is located is modulated and reconstructed to obtain the self-organized microturbulent system of gradient-split flow.

[0032] The subharmonic frequency excitation of the shear layer of the shear jet to obtain the phase-synchronized vortex ring sequence of the shear jet includes: The flow field characteristics of the shear jet are simultaneously acquired using multiple parameters to obtain the multi-scale flow characteristic parameters of the shear jet; Based on the multi-scale flow characteristic parameters, the optimal subharmonic excitation frequency of the shear layer in the shear jet is calculated, wherein the formula for calculating the optimal subharmonic excitation frequency is: ; In the formula, The optimal subharmonic excitation frequency is... The dominant frequency in the multi-scale flow characteristic parameters is... The convection velocity of the shear layer in the multi-scale flow characteristic parameters is... The root mean square velocity of the flow pulsation velocity in the shear layer is one of the multi-scale flow characteristic parameters. The dominant frequency in the multi-scale flow characteristic parameters is the turbulent kinetic energy spectral density value. This refers to the turbulent kinetic energy spectral density value at half the dominant frequency in the multi-scale flow characteristic parameters. The natural logarithm operator. The Reynolds number is the characteristic parameter of the multi-scale flow. The preset critical Reynolds number, Pi is a constant. is the base of the natural logarithm; Based on the optimal subharmonic excitation frequency, the shear layer is periodically perturbed and embedded to obtain the shear layer waveform of the shear jet; Based on the shear layer waveform, the shear jet is self-organized and coiled to obtain the phase-synchronized vortex ring sequence of the shear jet.

[0033] When simultaneously mapping the pressure and velocity fields of the gradient diversion, pressure monitoring elements and velocity monitoring elements are uniformly deployed in the flow area of ​​the gradient diversion to collect pressure and velocity data at each monitoring point in real time. The collected pressure and velocity data are then integrated in a spatiotemporal manner, and an image that can intuitively reflect the differences in hydraulic potential energy in different areas is drawn based on the integration results, thus obtaining a gradient distribution cloud map of real-time hydraulic potential energy in the gradient diversion.

[0034] When reshaping the streamlines of the gradient split based on the high potential energy continuous region of the gradient distribution cloud map, the shape of the guiding structure in the flow channel is adjusted with reference to the position and range of the high potential energy continuous region marked in the gradient distribution cloud map. This guides the fluid of the gradient split to flow directionally towards the high potential energy continuous region, making the flow path of the fluid more regular and concentrated, thus obtaining the guiding fluid of the gradient split.

[0035] When enriching the kinetic energy of the guiding fluid, the flow velocity of the fluid is increased by shrinking the cross-sectional size of the flow channel through which the guiding fluid flows, so that the kinetic energy of the fluid is effectively accumulated, forming a fluid bundle with high-speed flow characteristics, and thus obtaining a shear jet of the guiding fluid.

[0036] When simultaneously acquiring multiple parameters of the flow field characteristics of the shear jet, flow field monitoring elements are deployed along the flow path of the shear jet to collect relevant characteristic information such as flow velocity, flow direction, and turbulence intensity of the shear jet in real time. These characteristic information from different dimensions are systematically integrated to obtain the multi-scale flow characteristic parameters of the shear jet.

[0037] When determining the optimal subharmonic excitation frequency of the shear layer in the shear jet based on the multi-scale flow characteristic parameters, the excitation frequency that can generate stable periodic disturbances in the shear layer is matched by combining the shear jet main frequency characteristics, shear layer convection state and turbulent kinetic energy distribution reflected by the multi-scale flow characteristic parameters. This frequency is the optimal subharmonic excitation frequency.

[0038] When the shear layer is periodically perturbed and embedded based on the optimal subharmonic excitation frequency, a regular external perturbation is applied to the shear layer of the shear jet according to the determined optimal subharmonic excitation frequency, so that the flow state of the shear layer undergoes periodic fluctuation changes, forming a fluid layer structure with a specific fluctuation pattern, and thus obtaining the shear layer waveform of the shear jet.

[0039] When the shear jet is self-organized and coiled based on the shear layer waveform, the fluid inside the shear jet is guided to coil and merge in an orderly manner by relying on the periodic fluctuation law of the shear layer waveform, so that the fluid forms a series of vortex ring structures with consistent shape and synchronized phase, thus obtaining the phase-synchronized vortex ring sequence of the shear jet.

[0040] When modulating and reconstructing the geometric boundary of the local flow channel where the phase-synchronous vortex ring sequence is located, the inner wall shape and size parameters of the local flow channel are adjusted according to the distribution position and morphological characteristics of the phase-synchronous vortex ring sequence, so that the flow channel boundary is adapted to the flow characteristics of the vortex ring sequence, promoting the continuous and stable development of the vortex ring sequence, and obtaining the self-organized microturbulent system of gradient flow splitting.

[0041] Multi-parameter synchronous acquisition of the flow field characteristics of the shear jet yields multi-scale flow characteristic parameters. These multi-scale flow characteristic parameters are the sole data source for this calculation process. The calculation first requires obtaining the dominant frequency from these parameters. The dominant frequency is determined by analyzing the energy distribution of velocity fluctuation signals in the flow field and identifying the frequency component with the highest energy. The convective velocity is obtained by measuring the average distance traveled by vortex structures within the shear layer per unit time. The root mean square of the flow-direction fluctuation velocity is obtained by statistically analyzing the intensity of the flow-direction velocity fluctuation over time at a specific point in the flow field and calculating its standard deviation. The turbulent kinetic energy spectral density at the dominant frequency is obtained by performing spectral analysis on the flow-direction velocity signal and reading its energy intensity at the position corresponding to the dominant frequency in the spectrum. The turbulent kinetic energy spectral density at the half-frequency is obtained using the same spectral analysis method, reading its energy intensity at the frequency position corresponding to half of the dominant frequency. The Reynolds number based on momentum thickness is obtained by multiplying the momentum thickness calculated from velocity profile data, the aforementioned convection velocity, and the known kinematic viscosity of the fluid, and then dividing by the kinematic viscosity. The preset critical Reynolds number is a constant predetermined according to fluid stability theory. Pi is a known mathematical constant. The base of the natural logarithm is also a known mathematical constant.

[0042] The calculated optimal subharmonic excitation frequency was used to periodically perturb the shear layer. Periodic perturbation embedding is a technique that applies external excitation to the shear layer using this specific frequency. This excitation resonates with the unstable modes within the shear layer, causing instability at the shear layer interface and producing regular waveform curling. Thus, the product of this technique is the shear layer waveform of the shear jet, which is an unstable shear layer interface morphology with regular curling characteristics.

[0043] The beneficial effects include: by simultaneously mapping the gradient pressure and velocity fields of the diverted flow, accurate real-time gradient distribution cloud maps of hydraulic potential energy can be obtained. Streamline reshaping operations based on the high-potential-energy continuous region can guide the directional flow of fluid to form a regular guiding fluid. Enriching the kinetic energy of the guiding fluid can effectively increase fluid velocity and accumulate kinetic energy, forming a shear jet with high-speed flow characteristics. Simultaneous acquisition of multiple parameters of the shear jet flow field characteristics can completely obtain multi-scale flow characteristic parameters. Based on the optimal subharmonic excitation frequency matched to these parameters, regular perturbations can be applied to the shear layer to form a stable shear layer waveform. Self-organized vortex rings based on the shear layer waveform can promote the formation of a phase-synchronized vortex ring sequence. Modulation and reconstruction of the local channel geometry can adapt to the flow characteristics of the vortex ring sequence, ultimately forming a stable self-organized microturbulent system. This system can fully utilize the hydraulic potential energy of the wastewater itself, reduce external energy input, provide a high-quality flow field environment for subsequent wastewater treatment stages, and improve the overall energy utilization efficiency of the treatment process.

[0044] By synchronously acquiring flow field characteristics, a complete foundation of flow parameter data is obtained. Based on the dominant frequency energy distribution and flow stability state, the precise disturbance frequency is analyzed. Then, this frequency is used to periodically excite the shear layer to induce a regularly curled shear layer waveform. This waveform serves as the direct precursor for the subsequent generation of a phase-synchronous vortex ring sequence, effectively ensuring the reliability and efficiency of the self-organized microturbulence system construction. This lays a key technical foundation for achieving energy-efficient utilization and pollutant separation in wastewater treatment under low-energy conditions.

[0045] S04. The self-organized microturbulent system is subjected to nonlinear shear modulation to obtain the intelligent migration flocculants of the self-organized microturbulent system; In this embodiment of the invention, the step of performing nonlinear shear modulation on the self-organized microturbulent system to obtain intelligent migrating flocs of the self-organized microturbulent system includes: The rotation direction and angular velocity of the self-organized microturbulent system are identified by multiple parameters to obtain the vortex dynamics parameter set of the self-organized microturbulent system; Based on the vortex dynamics parameter set, the tangential velocity boundary layer of adjacent vortices in the self-organized microturbulent system is enhanced by converging, resulting in a high-intensity shear band in the self-organized microturbulent system. The fine particles suspended within the high-intensity shear band are driven by inertial force gradient to obtain the migrating particle group of the self-organized microturbulent system. The migrating particle group is polymer chain entangled to obtain the intelligent migrating flocculent of the self-organized microturbulent system.

[0046] When identifying the rotation direction and angular velocity of a self-organized microturbulent system using multiple parameters, a flow state monitoring component is used to fully cover the flow region of the self-organized microturbulent system. The detection end of the monitoring component directly contacts the fluid in the microturbulent system, capturing the direction of vortex rotation in each region in real time. At the same time, the time taken for a fixed point on the vortex to complete one complete cycle is recorded to determine the magnitude of the vortex angular velocity. The vortex rotation direction and angular velocity information obtained from all monitoring regions are classified and organized to form a set of vortex dynamic parameters for the self-organized microturbulent system.

[0047] Based on the obtained vortex dynamics parameter set, the matching relationship between the rotation direction and angular velocity of adjacent vortices in the self-organized microturbulent system is clarified. A flow-guiding protrusion structure is set at the flow channel position corresponding to the tangential velocity boundary layer of adjacent vortices. The shape and arrangement direction of the flow-guiding protrusion structure perfectly match the flow law reflected by the vortex dynamics parameter set, guide the boundary layer fluids of adjacent vortices to merge with each other and accelerate the flow velocity, strengthen the mutual shearing effect of the fluids in the boundary layer, and finally obtain the high-intensity shear zone of the self-organized microturbulent system.

[0048] When microparticles suspended within a high-intensity shear band are driven by inertial force gradient, the difference in shear force at different locations within the high-intensity shear band forms an inertial force gradient. Microparticles at different locations within the shear band are subjected to inertial forces of varying magnitudes. The inertial force propels the microparticles to move towards regions with stronger shear force. Microparticles originally dispersed within the shear band gradually converge towards specific regions under the continuous action of the inertial force gradient, ultimately forming a migrating particle swarm of a self-organized microturbulent system.

[0049] When polymer chain entanglement is performed on the migrating particle group, a polymer is quantitatively added into the flow channel where the migrating particle group is located. The polymer molecular chains are rapidly adsorbed on the surface of each fine particle in the migrating particle group. As the fluid continues to flow, the fine particles with adsorbed polymer molecular chains collide and contact each other. The polymer molecular chains on the surfaces of different particles intertwine and entangle with each other, connecting the dispersed fine particles into structurally stable flocculent aggregates, and finally obtaining a smart migrating flocculent of a self-organized microturbulent system.

[0050] The beneficial effects include: by conducting multi-parameter identification of the self-organized microturbulent system, accurately obtaining the vortex dynamic parameter set, and using this parameter set to enhance the convergence of the tangential velocity boundary layer of adjacent vortices, the fluid convergence effect can be strengthened to form a high-intensity shear band, improving the intensity and concentration of fluid shearing. Driven by the inertial force gradient within the high-intensity shear band, fine particles can be directed to aggregate and maintain a mobile state to form a migrating particle swarm, improving the aggregation and directional movement efficiency of fine particles. Introducing polymers into the migrating particle swarm to promote chain entanglement allows polymer molecular chains to attach to the particle surface and intertwine, connecting dispersed particles into a mobile polymeric structure, forming intelligent migrating flocs. This enhances the stability of particle aggregation and overall mobility, providing structurally regular and state-controllable treatment units for efficient advancement of subsequent treatment stages, and improving the aggregation and migration efficiency of pollutants during wastewater treatment.

[0051] S05. Perform multimodal parameter co-coding on the intelligent migration flocculant to obtain the process control command for the intelligent migration flocculant; In this embodiment of the invention, the step of performing multimodal parameter co-coding on the intelligent migration flocs to obtain process control instructions for the intelligent migration flocs includes: Simultaneous imaging of the spatial distribution density and surface potential distribution of the intelligent migrating flocs yields a dual-modal spatial distribution spectrum of the intelligent migrating flocs. Based on the dual-modal spatial distribution spectrum, the structural stability of the intelligent migratory flocs is divided by structural gradient to obtain the stability gradient of the intelligent migratory flocs. Based on the stability gradient, the focal position and intensity of energy application in the smart migration floc are reverse-engineered, and the derived information is used for strategy mapping to obtain the targeted energy application scheme of the smart migration floc. The targeted energy application scheme is compiled into an instruction sequence to obtain the process control instructions for the intelligent migration flocculant.

[0052] Based on the stability gradient, the focal position and intensity of energy application in the intelligent migratory flocs are inversely deduced, and the deduced information is used for strategy mapping to obtain a targeted energy application scheme for the intelligent migratory flocs, including: Based on the stability gradient, the baseline energy intensity value of the intelligent migrating flocculant is calculated, wherein the formula for calculating the baseline energy intensity value is: ; In the formula, The reference energy intensity value, The preset process constants for the intelligent migration flocculants, This is the maximum value of the stability gradient. The Gaussian error function is... Let be the mean of the stability gradient. Let be the standard deviation of the stability gradient. It is an exponential function. This is a preset value to prevent the division of small positive numbers into zero; By performing key point traversal detection on the spatial isosurface of the stability gradient, a candidate focal coordinate set of the intelligent migrating flocs is obtained. Based on the baseline energy intensity value and the candidate focus coordinate set, the smart migration flocs are spatiotemporally arranged to obtain a targeted energy application scheme for the smart migration flocs.

[0053] Simultaneous imaging of the spatial density and surface potential distribution of smart migrating flocs yields a dual-modal spatial distribution spectrum. This simultaneous imaging is achieved using a microscopic imaging system with dual-probe synchronous triggering. One probe is configured as a sensor array measuring turbidity or light scattering intensity, used to scan and acquire an image of the floc concentration distribution in space, representing the spatial density. The other probe is configured as a microelectrode potential scanning array, used to measure and acquire the potential distribution image of the floc surface at identical spatial coordinates and time points. The system fuses the two image data from the two probes, with strictly aligned spatial coordinates, to generate a composite spectrum containing both density and potential information for each pixel; this composite spectrum is the dual-modal spatial distribution spectrum.

[0054] Based on a bimodal spatial distribution spectrum, the structural stability of intelligent migrating flocs is determined by structural gradient partitioning, yielding a stability gradient. This structural gradient partitioning is achieved through the collaborative analysis of density and potential values ​​at each pixel in the bimodal spatial distribution spectrum. The analysis rules are pre-defined: regions with both high density and potential values ​​are considered to have high structural stability; regions with both low density and potential values, or with one significantly low value, are considered to have low structural stability. Following these rules, the system calculates and assigns a scalar value representing the stability of each pixel in the spectrum. Subsequently, the system arranges these scalar values ​​according to their spatial location, forming a new spatial distribution map containing only single-valued stability information. This new distribution map is called the stability gradient, which visually displays the changes in structural stability and spatial orientation at various points within the floc cluster.

[0055] Based on the stability gradient, the location and intensity of energy application focus in smart migration flocs are inversely extrapolated to obtain preliminary inference information. Inverse extrapolation is an analytical decision-making process. First, the system automatically identifies all continuous regions in the stability gradient map where the stability scalar value is below a preset threshold; these regions are marked as structurally vulnerable areas. Next, the system calculates the geometric center coordinates of each vulnerable area and preliminarily lists these coordinates as potential energy application focus locations. Simultaneously, based on the average stability scalar value within each vulnerable area and the size of that area, the system uses a pre-defined correspondence table to determine the basic energy intensity level required to apply to that focus. This dataset, which links the location of vulnerable areas to their corresponding intensities, constitutes the inference information.

[0056] The system performs strategy mapping on the post-simulation information to obtain a targeted energy application scheme for the intelligent migrating flocs. Strategy mapping is a process of optimizing and specifying the post-simulation information. The system checks the spatial distance between all initially determined focal points. If two focal points are too close, they are merged into one focal point based on their vulnerability, and the coordinates and intensity of the merged focal point are recalculated. Subsequently, according to the overall timing requirements of the processing technology, the system schedules a start-up time for each finally determined focal point to ensure that the energy application of multiple focal points is staggered in time to avoid interference and to spatially cover all critical vulnerable areas. Finally, the system generates a list containing a series of entries, each clearly recording the precise spatial coordinates of a focal point in the three-dimensional reactor, an energy intensity value, and a specific start-up time. This complete list constitutes the targeted energy application scheme.

[0057] The targeted energy application scheme is compiled into an instruction sequence to obtain process control instructions for intelligent migration flocculants. Instruction sequence compilation is the process of translating each entry in the targeted energy application scheme into command codes that downstream actuators can directly recognize and execute. The system reads each entry in the scheme list, converting spatial coordinates into pulse counts driving the 3D moving platform, energy intensity values ​​into power level codes for the energy generator, and start-up times into trigger signals for the system's internal clock. Then, the system combines all the converted device drive commands into a continuous command stream in chronological order. This command stream, after being encapsulated and formatted, becomes process control instructions that can be directly issued to hardware devices such as the moving platform, energy generator, and timing controller.

[0058] All parameters in the formula rely on the stability gradient of the smart migrating flocs generated in the preceding steps. The process constant is a fixed coefficient predetermined based on long-term experimental data regarding the material properties and fluid environment of this type of smart migrating flocs. The maximum gradient value is obtained by iterating through and comparing the scalar values ​​at all locations in the spatial distribution map of the stability gradient, finding the largest value. The gradient mean is obtained by summing the scalar values ​​at all locations in the stability gradient map and then dividing by the total number of locations. The gradient standard deviation is obtained by first calculating the difference between the scalar value at each location and the aforementioned gradient mean, squaring each difference, summing all squared values, dividing by the total number of locations, and finally taking the square root of the result. The zero-preset small positive number is an extremely small positive value pre-defined to prevent the denominator from being zero in mathematical calculations.

[0059] The significance of this calculation formula lies in comprehensively evaluating the overall characteristics of the stability gradient and mapping it to a unified baseline energy intensity value. The calculation process first assesses the deviation of the maximum gradient value from the gradient mean. This deviation, after normalization based on the square root of twice the gradient standard deviation, is transformed by a Gaussian error function. The transformation result is used to weighted correct the maximum value. Simultaneously, the calculation process assesses the non-uniformity of the gradient distribution by dividing the gradient standard deviation by the gradient mean plus a small positive number, and using this ratio as the negative exponent of an exponential function to obtain a decay coefficient. Finally, the weighted corrected maximum gradient value is multiplied by the process constant and the decay coefficient; the result is the baseline energy intensity value. This calculation process ensures that the baseline energy intensity value responds to the needs of the most unstable regions while being adjusted by the overall gradient uniformity.

[0060] The formula clearly shows the response of the baseline energy intensity value to changes in input parameters. When the maximum value in the stability gradient increases, the baseline energy intensity value increases accordingly. When the gradient mean increases, the baseline energy intensity value tends to decrease. When the gradient standard deviation increases, i.e., the gradient distribution becomes more uneven, the baseline energy intensity value will be correspondingly lowered through the exponential decay term. The entire calculation process nonlinearly integrates the three statistical characteristics—maximum value, mean, and standard deviation—through the coupling of the Gaussian error function and the exponential function. Its output trend guides the applied energy baseline intensity to focus on the weakest point while also considering the overall stability distribution of the structure, avoiding inaccuracies in energy setting due to local extreme values ​​or excessive overall dispersion.

[0061] The beneficial effects are that by acquiring a complete physical state map of the flocs through synchronous imaging and dividing it into a precise stability distribution gradient, a customized energy application strategy can be deduced based on this gradient and an energy application scheme with both spatial targeting and temporal sequence can be generated through strategy mapping. Finally, the scheme is compiled into a sequence of action instructions that can directly drive hardware devices to execute, thereby realizing quantitative, programmed and adaptive precise control of the floc dissociation process, which significantly improves the energy efficiency ratio and the consistency of the treatment effect in the separation process.

[0062] By extracting the statistical characteristics of the stability gradient and using nonlinear functions for comprehensive mapping, the dynamic relationship between the gradient extrema, average level and dispersion is transformed into a benchmark energy intensity value that can both focus on the weakest point of the structure and adaptively adjust according to the overall distribution uniformity. This achieves the accuracy and robustness of energy setting and effectively avoids control inaccuracies caused by local anomalies or overall inhomogeneity.

[0063] S06. Based on the process control instructions, the intelligent migration flocculants are precisely separated at the phase interface to obtain the purified effluent of the target wastewater to be treated. In this embodiment of the invention, the step of precisely separating the phase interface of the intelligent migrating flocculant based on the process control command to obtain the purified effluent of the target wastewater includes: Based on the process control command, focused energy injection is performed on the intelligent migration flocculants to obtain the dispersed flocculants of the self-organized microturbulent system; Hydraulic stratification was induced in the dispersed flocs to obtain the solid-liquid separation state of the self-organized microturbulent system; Based on the solid-liquid separation state, the aqueous phase of the self-organized microturbulent system is clarified and guided to obtain the purified effluent of the target wastewater to be treated.

[0064] Based on the obtained process control instructions, an energy emission component is used to inject energy into a specific area of ​​the smart migration flocculant. The position and intensity of the energy emission component fully comply with the requirements of the process control instructions. The energy is precisely applied to the molecular connection sites of the smart migration flocculant, breaking the polymer chain entanglement structure inside the flocculant and dispersing the originally aggregated flocculent into fine floc particles. These fine floc particles are evenly distributed in the fluid of the self-organized microturbulent system, ultimately resulting in a dispersed flocculant of the self-organized microturbulent system.

[0065] When hydraulic stratification is induced in the obtained dispersed flocs, an inclined guide plate is set in the flow channel where the dispersed flocs are located. The direction of the guide plate is at a specific angle to the direction of fluid flow, guiding the fluid to flow slowly. Under the action of gravity, the solid particles in the dispersed flocs gradually settle to the bottom of the flow channel, while the fluid gathers to the upper part of the flow channel under the guidance of the guide plate. With the continuous guidance of the guide plate, a clear stratification gradually forms in the flow channel, with the upper layer being the water phase and the lower layer being solid particles. The solid particles and the water phase are clearly separated, and finally the solid-liquid separation state of the self-organized microturbulent system is obtained.

[0066] Based on the obtained solid-liquid separation state, a clarification zone is set in the upper part of the flow channel. A filter membrane is installed inside the clarification zone. The pore size of the filter membrane is large enough to trap the tiny flocculent particles remaining in the water. The water phase in the upper layer of the flow channel flows naturally into the clarification zone under the action of hydraulic force. After being trapped and filtered by the filter membrane, the tiny particles in the water are completely blocked, and the water phase in the clarification zone becomes clear and transparent. The clear water phase is then discharged through the guide channel at the end of the clarification zone. The discharged water phase is the purified effluent of the target wastewater to be treated.

[0067] The beneficial effect is the precise phase interface separation operation performed on intelligent migrating flocs based on process control commands. By focusing energy injection, it precisely targets the molecular connection sites of the intelligent migrating flocs, efficiently breaking down their internal polymer chain entanglement structure. This causes the aggregated flocs to disperse into fine floc particles, forming dispersed flocs, laying a solid foundation for subsequent solid-liquid separation. An inclined guide plate guides the fluid to flow slowly, using gravity to push the solid particles in the dispersed flocs to the bottom of the channel, while simultaneously guiding the fluid to converge at the top, achieving clear solid-liquid stratification and a stable solid-liquid separation state. The filter membrane in the clarification zone traps residual tiny floc particles in the water, and the clear water phase is then discharged through the guide channel, resulting in purified effluent. The entire process requires no additional complex operations, has high separation accuracy, effectively ensures the quality of the purified effluent, and significantly improves the overall efficiency and quality of wastewater treatment.

[0068] like Figure 2 The diagram shown is a functional block diagram of a low-energy wastewater treatment system provided in an embodiment of the present invention.

[0069] The low-energy wastewater treatment system 10 described in this invention can be installed in an electronic device. Depending on the functions implemented, the low-energy wastewater treatment system 10 may include a gradient diversion pretreatment module 11, a load spatiotemporal characteristic analysis module 12, an energy self-consistent turbulence generation module 13, an intelligent flocculant modulation module 14, an adaptive decision control module 15, and a phase interface precise separation module 16. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0070] In this embodiment, the functions of each module / unit are as follows: The gradient diversion pretreatment module 11 is used to perform multi-stage cyclone separation on the target wastewater to obtain the gradient diversion of the target wastewater. The load spatiotemporal feature analysis module 12 is used to perform spatiotemporal correlation analysis on the load distribution of the gradient-based load splitting to obtain the distribution features of the gradient-based load splitting. The energy self-consistent turbulence generation module 13 is used to perform energy self-consistent transformation on the gradient split based on the distribution characteristics, so as to obtain the self-organized microturbulence system of the gradient split; The intelligent floc modulation module 14 is used to perform nonlinear shear modulation on the self-organized microturbulent system to obtain intelligent migration flocs of the self-organized microturbulent system. The adaptive decision control module 15 is used to perform multimodal parameter co-encoding on the intelligent migration flocculant to obtain the process control command of the intelligent migration flocculant. The phase interface precision separation module 16 is used to perform precise phase interface separation on the intelligent migration flocs based on the process control instructions, so as to obtain the purified effluent of the target wastewater to be treated.

[0071] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0072] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0074] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0075] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A low-energy wastewater treatment method, characterized in that, The method includes: S01. Perform multi-stage cyclone separation on the target wastewater to be treated to obtain a gradient diversion of the target wastewater to be treated; S02. Perform spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting; S03. Based on the distribution characteristics, perform energy self-consistent transformation on the gradient split to obtain the self-organized microturbulent system of the gradient split; S04. The self-organized microturbulent system is subjected to nonlinear shear modulation to obtain the intelligent migration flocculants of the self-organized microturbulent system; S05. Perform multimodal parameter co-coding on the intelligent migration flocculant to obtain the process control command for the intelligent migration flocculant; S06. Based on the process control instructions, the intelligent migration flocculants are precisely separated at the phase interface to obtain the purified effluent of the target wastewater to be treated.

2. The low-energy wastewater treatment method as described in claim 1, characterized in that, The step of performing multi-stage cyclone separation on the target wastewater to obtain a gradient diversion of the target wastewater includes: The original flow regime characteristics of the target wastewater to be treated are identified to obtain the fluid dynamic fingerprint of the target wastewater to be treated; Based on the fluid dynamic fingerprint, the target wastewater to be treated is structured into layers, and the layered wastewater is subjected to continuous phase centrifugal separation to obtain the intermediate separated flow of the target wastewater to be treated. The intermediate separated flow is subjected to vortex core-edge co-precipitation to obtain the gradient separation of the target wastewater to be treated.

3. The low-energy wastewater treatment method as described in claim 1, characterized in that, The process of performing spatiotemporal correlation analysis on the load distribution of the gradient-based traffic splitting to obtain the distribution characteristics of the gradient-based traffic splitting includes: The gradient-based traffic splitting is continuously tracked and located to obtain the load evolution segments of the gradient-based traffic splitting; Based on the load evolution segment, the path dependence of the pollution load in the gradient diversion is identified to obtain the load transfer trajectory of the gradient diversion. Spatial hub analysis is performed on the interaction of the load transfer trajectories to obtain the aggregation nodes and diversion nodes of the pollution load; Based on the aggregation node and the splitting node, multimodal feature integration is performed on the gradient splitting to obtain the distribution characteristics of the gradient splitting.

4. The low-energy wastewater treatment method as described in claim 1, characterized in that, Based on the distribution characteristics, the gradient flow is subjected to self-consistent energy conversion to obtain a self-organized microturbulent system of the gradient flow, including: The pressure field and velocity field of the gradient diversion are simultaneously mapped to obtain a gradient distribution cloud map of the real-time hydraulic potential energy in the gradient diversion. Based on the high potential energy continuous region of the gradient distribution cloud map, the streamline of the gradient split is reshaped to obtain the guiding fluid of the gradient split; Kinetic energy enrichment is performed on the guiding fluid to obtain a shear jet of the guiding fluid; The shear layer of the shear jet is excited at a subharmonic frequency to obtain a phase-synchronous vortex ring sequence of the shear jet; The geometric boundary of the local flow channel where the phase-synchronous vortex ring sequence is located is modulated and reconstructed to obtain the self-organized microturbulent system of gradient-split flow.

5. The low-energy wastewater treatment method as described in claim 4, characterized in that, The subharmonic frequency excitation of the shear layer of the shear jet to obtain the phase-synchronized vortex ring sequence of the shear jet includes: The flow field characteristics of the shear jet are simultaneously acquired using multiple parameters to obtain the multi-scale flow characteristic parameters of the shear jet; Based on the multi-scale flow characteristic parameters, the optimal subharmonic excitation frequency of the shear layer in the shear jet is calculated, wherein the formula for calculating the optimal subharmonic excitation frequency is: ； In the formula, The optimal subharmonic excitation frequency is... The dominant frequency in the multi-scale flow characteristic parameters is... The convection velocity of the shear layer in the multi-scale flow characteristic parameters is... The root mean square velocity of the flow pulsation velocity in the shear layer is one of the multi-scale flow characteristic parameters. The dominant frequency in the multi-scale flow characteristic parameters is the turbulent kinetic energy spectral density value. This refers to the turbulent kinetic energy spectral density value at half the dominant frequency in the multi-scale flow characteristic parameters. The natural logarithm operator. The Reynolds number is the characteristic parameter of the multi-scale flow. The preset critical Reynolds number, Pi is a constant. is the base of the natural logarithm; Based on the optimal subharmonic excitation frequency, the shear layer is periodically perturbed and embedded to obtain the shear layer waveform of the shear jet; Based on the shear layer waveform, the shear jet is self-organized and coiled to obtain the phase-synchronized vortex ring sequence of the shear jet.

6. The low-energy wastewater treatment method as described in claim 1, characterized in that, The process of nonlinearly shearing and modulating the self-organized microturbulent system to obtain intelligent migrating flocs of the self-organized microturbulent system includes: The rotation direction and angular velocity of the self-organized microturbulent system are identified by multiple parameters to obtain the vortex dynamics parameter set of the self-organized microturbulent system; Based on the vortex dynamics parameter set, the tangential velocity boundary layer of adjacent vortices in the self-organized microturbulent system is enhanced by converging, resulting in a high-intensity shear band in the self-organized microturbulent system. The fine particles suspended within the high-intensity shear band are driven by inertial force gradient to obtain the migrating particle group of the self-organized microturbulent system. The migrating particle group is polymer chain entangled to obtain the intelligent migrating flocculent of the self-organized microturbulent system.

7. The low-energy wastewater treatment method as described in claim 1, characterized in that, The process control instructions for the intelligent migration flocs, obtained by multimodal parameter co-encoding, include: Simultaneous imaging of the spatial distribution density and surface potential distribution of the intelligent migrating flocs yields a dual-modal spatial distribution spectrum of the intelligent migrating flocs. Based on the dual-modal spatial distribution spectrum, the structural stability of the intelligent migratory flocs is divided by structural gradient to obtain the stability gradient of the intelligent migratory flocs. Based on the stability gradient, the focal position and intensity of energy application in the smart migration floc are reverse-engineered, and the derived information is used for strategy mapping to obtain the targeted energy application scheme of the smart migration floc. The targeted energy application scheme is compiled into an instruction sequence to obtain the process control instructions for the intelligent migration flocculant.

8. The low-energy wastewater treatment method as described in claim 7, characterized in that, Based on the stability gradient, the focal position and intensity of energy application in the intelligent migratory flocs are inversely deduced, and the deduced information is used for strategy mapping to obtain a targeted energy application scheme for the intelligent migratory flocs, including: Based on the stability gradient, the baseline energy intensity value of the intelligent migrating flocculant is calculated, wherein the formula for calculating the baseline energy intensity value is: ； In the formula, The reference energy intensity value, The preset process constants for the intelligent migration flocculants, This is the maximum value of the stability gradient. The Gaussian error function is... Let be the mean of the stability gradient. Let be the standard deviation of the stability gradient. It is an exponential function. This is a preset value to prevent the division of small positive numbers into zero; By performing key point traversal detection on the spatial isosurface of the stability gradient, a candidate focal coordinate set of the intelligent migrating flocs is obtained. Based on the baseline energy intensity value and the candidate focus coordinate set, the smart migration flocs are spatiotemporally arranged to obtain a targeted energy application scheme for the smart migration flocs.

9. The low-energy wastewater treatment method as described in claim 1, characterized in that, The process of precisely separating the phase interface of the intelligent migrating flocculants based on the process control instructions to obtain the purified effluent of the target wastewater to be treated includes: Based on the process control command, focused energy injection is performed on the intelligent migration flocculants to obtain the dispersed flocculants of the self-organized microturbulent system; Hydraulic stratification was induced in the dispersed flocs to obtain the solid-liquid separation state of the self-organized microturbulent system; Based on the solid-liquid separation state, the aqueous phase of the self-organized microturbulent system is clarified and guided to obtain the purified effluent of the target wastewater to be treated.

10. A low-energy wastewater treatment system, characterized in that, For implementing the low-energy wastewater treatment method according to claim 1, the system comprises: The gradient diversion pretreatment module is used to perform multi-stage cyclone separation on the target wastewater to obtain the gradient diversion of the target wastewater; The load spatiotemporal feature analysis module is used to perform spatiotemporal correlation analysis on the load distribution of the gradient-based load splitting to obtain the distribution features of the gradient-based load splitting. An energy self-consistent turbulence generation module is used to perform energy self-consistent transformation on the gradient split based on the distribution characteristics, so as to obtain the self-organized microturbulent system of the gradient split; The intelligent floc modulation module is used to perform nonlinear shear modulation on the self-organized microturbulent system to obtain intelligent migration flocs of the self-organized microturbulent system; An adaptive decision control module is used to perform multimodal parameter co-encoding on the intelligent migration flocculant to obtain process control instructions for the intelligent migration flocculant. The phase interface precision separation module is used to perform precise phase interface separation on the intelligent migration flocs based on the process control instructions, so as to obtain the purified effluent of the target wastewater to be treated.

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