Intelligent control method and system for efficient oil-water separation based on physical demulsification

Abstract

The application provides an efficient oil-water separation intelligent control method and system based on physical demulsification, relates to the field of oil-water separation, and comprises the following steps: detecting physical parameters of oil droplets, calculating a physical field distribution gradient, calculating a target migration path according to an initial motion state, adjusting a physical driving force in real time, adjusting a physical field output by adopting a physical field compensation function and a cooperative control mechanism, and determining an optimal timing ratio, so that intelligent control and efficient operation of the oil-water separation process are realized, and the oil-water separation efficiency and separation purity are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to oil-water separation technology, and in particular to an efficient oil-water separation intelligent control method and system based on physical demulsification. BACKGROUND

[0002] Oil-containing wastewater treatment is an important environmental problem faced by the petrochemical industry, food processing and marine pollution control. Traditional oil-water separation technologies mainly include gravity sedimentation, air flotation, chemical demulsification and membrane separation. With the expansion of industrial production scale and the improvement of environmental protection standards, more efficient and intelligent oil-water separation technologies need to be developed. Physical demulsification method has become a research hotspot due to its environmental protection, high efficiency and strong controllability. Oil-water separation technology under the synergistic effect of physical fields uses electric field, acoustic field, magnetic field and other physical field forces to exert directional driving force on oil droplets, promotes oil droplet coalescence and migration, and realizes rapid oil-water separation.

[0003] The existing oil-water separation technology based on physical demulsification lacks accurate real-time monitoring mechanism of oil droplet physical parameters, and cannot dynamically adjust the physical field parameters according to the specific characteristics of oil-containing wastewater, resulting in low processing efficiency and high energy consumption. Most systems use fixed parameter settings, which cannot cope with changes in the composition and concentration of oil-containing wastewater, and the processing effect is unstable. And there is a lack of synergistic control mechanism between the existing technologies of various physical fields, which often run independently or simply superimpose, and fail to fully exert the synergistic effect of multiple physical fields. When the oil droplet motion trajectory deviates from the expected path, the system lacks effective compensation mechanism, making it difficult to achieve precise oil droplet migration control and affecting the separation efficiency. At the same time, there is a lack of intelligent control strategy based on the coalescence degree of oil droplets, and the physical field energy input lacks optimal configuration, resulting in energy waste and equipment wear and tear. The system cannot automatically adjust the output power and timing ratio of each physical field according to the actual coalescence state of the oil droplets, making it difficult to achieve efficient use of energy while ensuring separation efficiency. SUMMARY

[0004] The embodiments of the present application provide an efficient oil-water separation intelligent control method and system based on physical demulsification, which can solve the problems in the prior art.

[0005] In a first aspect, the embodiments of the present application provide an efficient oil-water separation intelligent control method based on physical demulsification, comprising:

[0006] Detecting the physical parameters of oil droplets in oil-containing wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of oil droplets;

[0007] According to the initial motion state, the target migration path of the oil droplets is calculated, the output size and spatial distribution of the physical driving force are adjusted, the oil droplets are moved to the target migration path, and the actual motion trajectory of the oil droplets is collected in real time. The deviation value between the actual motion trajectory and the target migration path is calculated;

[0008] The compensation function of the physical field is established according to the deviation value, the output intensity of the physical field is adjusted in combination with the motion parameters of the oil droplets, and the cooperative control mechanism of the physical field is triggered when the deviation value exceeds a preset deviation range;

[0009] The instantaneous motion state and the coalescence degree of the oil droplets are collected, the energy input sequence of each physical field is calculated based on the compensation function and the cooperative control mechanism of the physical field, and the optimal timing ratio between the physical fields is determined;

[0010] The output power of each physical field is adjusted according to the optimal timing ratio, the coalescence size of the oil droplets is monitored in real time, and when the coalescence size reaches a preset value, the physical fields are gradually closed according to the energy input sequence, and the oil-water separation process is completed.

[0011] In an alternative embodiment,

[0012] The physical parameters of the oil droplets in the oil-containing wastewater are detected, the distribution gradient of each physical field is calculated, and the initial motion state of the oil droplets is obtained, including:

[0013] A microelectrode matrix is arranged on the wall surface of the flow channel, the surface of the oil droplets in the oil-containing wastewater is scanned by the microelectrode matrix, and the potential distribution around the oil droplets is obtained;

[0014] A temperature sensor network is arranged in the flow channel, the temperature distribution around the oil droplets is obtained, and the dynamic viscosity of the oil-containing wastewater is measured by a viscometer, and the viscous drag coefficient in the flow channel is calculated according to the dynamic viscosity and the shape parameters of the oil droplets;

[0015] The electric field gradient around the oil droplets and the electric field force on the oil droplets are calculated according to the potential distribution, the temperature gradient around the oil droplets and the thermophoretic force on the oil droplets are calculated according to the temperature distribution, and the oil droplet resultant force is obtained by vector superposition of the electric field force and the thermophoretic force;

[0016] The force size and direction on the oil droplets are calculated according to the oil droplet resultant force and the viscous drag coefficient, the motion speed and acceleration of the oil droplets are obtained, and the initial motion state of the oil droplets is determined, including the initial position and initial motion parameters of the oil droplets.

[0017] In an alternative embodiment,

[0018] The target migration path of the oil droplets is calculated according to the initial motion state, the output size and spatial distribution of the physical driving force are adjusted to make the oil droplets move towards the target migration path, and the actual motion trajectory of the oil droplets is collected in real time, and the deviation between the actual motion trajectory and the target migration path is calculated, including:

[0019] constructing a motion characteristic parameter matrix of the oil droplet based on the initial motion state, calculating motion trajectories of the oil droplet under different physical field intensity and spatial distribution conditions by using the motion characteristic parameter matrix, and obtaining multiple predicted paths with different driving force distributions;

[0020] performing kinematic analysis on each predicted path to obtain a velocity change curve and an acceleration change curve of each predicted path, calculating driving energy consumption and time consumption of each predicted path according to the velocity change curve and the acceleration change curve, establishing an evaluation function with the objective of minimizing the driving energy consumption and the time consumption, calculating an evaluation score of each predicted path based on the evaluation function, and selecting a path with the highest evaluation score as a target migration path;

[0021] dividing the target migration path into multiple continuous control intervals according to the motion characteristics of the oil droplet, adjusting the output size and spatial distribution of the physical driving force in each control interval, and enabling the oil droplet to move towards the target migration path;

[0022] real-time collection of instantaneous positions and velocities of the oil droplet, establishment of an actual motion trajectory spatial curve based on a time sequence, segmented projection mapping of the actual motion trajectory spatial curve and the target migration path, and obtaining a deviation between the actual motion trajectory and the target migration path by calculating the Euclidean distance between corresponding projection points.

[0023] In an alternative embodiment,

[0024] real-time collection of instantaneous positions and velocities of the oil droplet, establishment of an actual motion trajectory spatial curve based on a time sequence, segmented projection mapping of the actual motion trajectory spatial curve and the target migration path, and obtaining a deviation between the actual motion trajectory and the target migration path by calculating the Euclidean distance between corresponding projection points.

[0025] collection of instantaneous position information of the oil droplet during the motion process, and conversion of the instantaneous position information into discrete spatial coordinate points under a time sequence;

[0026] establishment of an initial point set by using the discrete spatial coordinate points, data smoothing and removal of abnormal values of the initial point set to obtain a corrected discrete spatial coordinate sequence, and generation of a continuous trajectory spatial curve representing the actual motion of the oil droplet by performing cubic spline interpolation fitting based on the corrected discrete spatial coordinate sequence;

[0027] selection of feature control points on the target migration path according to the curvature variation law, establishment of a feature control point sequence, calculation of normal projection points of each point on the continuous trajectory spatial curve to the target migration path, and establishment of a spatial mapping pairing relationship between the normal projection points and the nearest feature control points;

[0028] Based on the spatial mapping pairing relationship, the Euclidean distance between each pair of mapping points is calculated, the Euclidean distance is taken as the deviation value of the actual motion trajectory at the current position and the target migration path, and a complete trajectory deviation distribution is obtained.

[0029] In an alternative embodiment,

[0030] A compensation function of the physical field is established according to the deviation value, the output intensity of the physical field is adjusted in combination with the motion parameters of the oil droplets, and a cooperative control mechanism of the physical field is triggered when the deviation value exceeds a preset deviation range, including:

[0031] The time differential operation is performed on the deviation value to obtain a deviation change rate, a compensation function of the physical field is established according to the deviation value and the deviation change rate, and a position compensation coefficient and a velocity compensation coefficient are set based on the variation trend of the deviation value;

[0032] The position compensation coefficient and the velocity compensation coefficient are used to adjust the compensation function of the physical field, a compensation force of the physical field is obtained, and the compensation force of the physical field is decomposed to obtain an electric field compensation force and a temperature field compensation force;

[0033] In combination with the motion parameters of the oil droplets, the output intensity of the physical field is adjusted according to the electric field compensation force and the temperature field compensation force, when the deviation value exceeds a preset deviation range, the switching timing of the physical field is determined according to the characteristic oscillation frequency of the oil droplets, the output intensity of the electric field and the temperature field is alternately adjusted according to the switching timing, and the cooperative control mechanism of the physical field is triggered.

[0034] In an alternative embodiment,

[0035] The instantaneous motion state and the coalescence degree of the oil droplets are collected, the energy input sequence of each physical field is calculated based on the compensation function and the cooperative control mechanism of the physical field, and the optimal timing ratio between the physical fields is determined, including:

[0036] The oil droplet image is acquired, the instantaneous motion state of the oil droplets is reconstructed through the image depth information, including the spatial position coordinates, the motion velocity and the acceleration of the oil droplets, and the edge of the oil droplet image is extracted, the local curvature distribution of the oil droplet interface is calculated, the instantaneous coalescence degree of the oil droplets is acquired according to the change amount of the interface curvature at adjacent time points and the curvature variation trend of the oil droplet motion trajectory;

[0037] According to the instantaneous motion state and the instantaneous coalescence degree, the compensation coefficient is determined in the preset physical field compensation function, and the cooperative control mechanism of the physical field is triggered, and the compensation amount of each physical field is calculated based on the physical field compensation function;

[0038] The instantaneous motion state change and instantaneous coalescence degree change of the oil droplets under the compensation amount of each physical field are calculated, the energy conversion efficiency of each physical field is calculated, and the energy input sequence of the electric field, the acoustic field and the temperature field is calculated based on the energy conversion efficiency and the change trend of the instantaneous coalescence degree.

[0039] The difference between the maximum value and the minimum value of each physical field energy sequence in the energy input sequence is calculated to obtain the energy fluctuation amplitude of the physical field, and the optimal timing ratio of the physical field is determined according to the order from small to large of the energy fluctuation amplitude.

[0040] In an alternative embodiment,

[0041] The output power of each physical field is adjusted according to the optimal timing ratio, the coalescence size of the oil droplets is monitored in real time, and when the coalescence size reaches a preset value, the physical field is gradually turned off according to the energy input sequence, and the oil-water separation process is completed.

[0042] The output power of each physical field is adjusted to the corresponding value of the energy input sequence according to the optimal timing ratio, and the output power of the electric field, the acoustic field and the temperature field is adjusted according to the proportion of the energy input sequence, and the adjustment order of the output power of each physical field is controlled based on the optimal timing ratio.

[0043] The coalescence size of the oil droplets is monitored in real time and processed by time window averaging to obtain the average coalescence size of the oil droplets, and when the average coalescence size of the oil droplets reaches a preset size value, the physical field is turned off according to the order of the energy fluctuation amplitude in the optimal timing ratio.

[0044] The average coalescence size of the oil droplets is monitored within the closing time interval of the adjacent physical field, and when the average coalescence size of the oil droplets exceeds a preset size range, the closed physical field is started and the power is adjusted to a set proportion of the corresponding value of the energy input sequence, and the average coalescence size of the oil droplets is monitored, and when the average coalescence size of the oil droplets is within the preset size range and lasts for a preset time, the oil-water separation process is completed.

[0045] In a second aspect of the embodiment of the present application, an efficient oil-water separation intelligent control system based on physical demulsification is provided, comprising:

[0046] The first unit is used for detecting the physical parameters of the oil droplets in the oil-containing wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets.

[0047] The second unit is used for calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force, making the oil droplets move towards the target migration path, and collecting the actual motion trajectory of the oil droplets in real time, and calculating the deviation value between the actual motion trajectory and the target migration path.

[0048] The third unit is used to establish a compensation function for the physical field based on the deviation value, and adjust the output intensity of the physical field in combination with the motion parameters of the oil droplets. When the deviation value exceeds the preset deviation range, the collaborative control mechanism of the physical field is triggered.

[0049] The fourth unit is used to collect the instantaneous motion state and aggregation degree of oil droplets. Based on the compensation function and cooperative control mechanism of the physical field, it calculates the energy input sequence of each physical field and determines the optimal temporal matching between the physical fields.

[0050] The fifth unit is used to adjust the output power of each physical field according to the optimal timing ratio, monitor the coalescence size of oil droplets in real time, and shut down the physical fields step by step according to the energy input sequence when the coalescence size reaches the preset value, thus completing the oil-water separation process.

[0051] A third aspect of the present invention provides an electronic device, comprising:

[0052] processor;

[0053] Memory used to store processor-executable instructions;

[0054] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0055] 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.

[0056] In this embodiment, by real-time detection of the physical parameters and motion state of oil droplets, combined with the calculation of the physical field distribution gradient, precise control of the trajectory of oil droplets in oily wastewater is achieved, significantly improving the accuracy and efficiency of oil-water separation and solving the technical problem of uncontrollable oil droplet motion in traditional physical demulsification methods. By establishing a physical field compensation function and combining it with a collaborative control mechanism, the output intensity and spatial distribution of the physical driving force can be intelligently adjusted, achieving dynamic correction of the oil droplet migration path. This effectively addresses the complex operating conditions of different oil-water mixtures, improving the system's adaptability and stability. By calculating the physical field energy input sequence and optimal timing ratio, the synergistic effect of multiple physical fields and optimized energy configuration are achieved. This significantly reduces energy consumption while ensuring sufficient oil droplet aggregation, improving equipment operating efficiency and demonstrating significant economic and environmental benefits. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the efficient oil-water separation intelligent control method based on physical demulsification, as described in an embodiment of the present invention.

[0058] Figure 2A flow chart of the multi-physical field synergistic control process for the oil-water separation of the embodiment of the present application. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0060] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments can not be described again for the same or similar concepts or processes.

[0061] Figure 1 A flow chart of the intelligent control method for high-efficiency oil-water separation based on physical demulsification of the embodiment of the present application is shown in FIG. 1. Figure 1 The method comprises the following steps.

[0062] Detecting the physical parameters of oil droplets in the oil-containing wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets.

[0063] Calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force, making the oil droplets move towards the target migration path, and collecting the actual motion trajectory of the oil droplets in real time, and calculating the deviation value between the actual motion trajectory and the target migration path.

[0064] Establishing a compensation function of the physical field according to the deviation value, adjusting the output intensity of the physical field in combination with the motion parameters of the oil droplets, and triggering the synergistic control mechanism of the physical field when the deviation value exceeds a preset deviation range.

[0065] Collecting the instantaneous motion state and the coalescence degree of the oil droplets, calculating the energy input sequence of each physical field based on the compensation function of the physical field and the synergistic control mechanism, and determining the optimal time sequence ratio between the physical fields.

[0066] Adjusting the output power of each physical field according to the optimal time sequence ratio, monitoring the coalescence size of the oil droplets in real time, and gradually closing the physical fields according to the energy input sequence when the coalescence size reaches a preset value, thereby completing the oil-water separation process.

[0067] In an alternative embodiment, detecting the physical parameters of oil droplets in the oil-containing wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets comprises:

[0068] A microelectrode matrix is arranged on the wall surface of the flow channel. The surface of the oil droplet in the oil-containing wastewater is scanned by the microelectrode matrix, and the potential distribution around the oil droplet is obtained.

[0069] A temperature sensor network is arranged in the flow channel to obtain the temperature distribution around the oil droplet. The dynamic viscosity of the oil-containing wastewater is measured by a viscometer, and the viscous drag coefficient in the flow channel is calculated according to the dynamic viscosity and the shape parameters of the oil droplet.

[0070] The electric field gradient around the oil droplet and the electric field force on the oil droplet are calculated according to the potential distribution. The temperature gradient around the oil droplet and the thermophoretic force on the oil droplet are calculated according to the temperature distribution. The electric field force and the thermophoretic force are vector superimposed to obtain the resultant force of the oil droplet.

[0071] The magnitude and direction of the force acting on the oil droplet are calculated according to the resultant force of the oil droplet and the viscous drag coefficient. The motion speed and acceleration of the oil droplet are obtained, and the initial motion state of the oil droplet is determined, including the initial position and initial motion parameters of the oil droplet.

[0072] In the process of detecting the physical parameters of the oil droplet, a microelectrode matrix is arranged on the wall surface of the flow channel. The microelectrode matrix is composed of a plurality of micron-level electrode units, each electrode unit has a size of about 50μm×50μm, and the spacing between adjacent electrodes is 20μm to ensure the spatial resolution of the potential scanning. The microelectrode matrix is powered by a direct current power supply, with a voltage range of 0.5 to 5V and a scanning frequency of 10Hz. When the oil droplet passes through the flow channel, the microelectrode matrix scans the surface of the oil droplet and collects the potential signals around the oil droplet. The collected potential data is amplified by a signal amplifier and converted into digital signals by an analog-to-digital converter, and then enters the data processing unit. The data processing unit reconstructs the three-dimensional potential distribution around the oil droplet by interpolation algorithm. In practical application, the potential value measured at a distance of 10μm from the surface of an oil droplet with a diameter of 100μm is usually between 0.2 and 0.8V, which can be used for subsequent electric field gradient calculation.

[0073] The arrangement of the temperature sensor network is crucial for obtaining the temperature distribution around the oil droplet. The network is composed of a micro-thermistor array, with a sensor size of 30μm×30μm, a measurement accuracy of ±0.1℃, and a response time of less than 10ms. The sensor array is distributed in a grid-like manner in the flow channel, with a grid spacing of 100μm, covering the key areas of the flow channel. Each sensor collects real-time temperature data at its location, with a sampling frequency of 20Hz. The temperature data is transmitted to the data processing unit through a dedicated interface, and the temperature field distribution in the flow channel is reconstructed by a three-dimensional interpolation algorithm. In a typical application scenario, the temperature gradient in the flow channel is about 0.5 to 2℃ / mm, which is sufficient to produce a significant thermophoretic effect.

[0074] Meanwhile, a viscometer is installed in the flow channel to measure the dynamic viscosity of the oil-containing wastewater. The viscometer used is a micro rotary viscometer with a measurement range of 0.5 to 100 mPa s and an accuracy of ±1%. The viscometer measures the viscosity value of the wastewater every 5 seconds and transmits the data to the control system. Based on the measured dynamic viscosity value and the shape parameter of the oil droplet, the viscous drag coefficient in the flow channel is calculated. The oil droplet shape parameter is obtained through a high-speed camera system with a resolution of 1200x1200 pixels and a frame rate of 500 fps. The image processing algorithm analyzes the shape characteristics of the oil droplet such as the ratio of the major and minor axes and the surface curvature, and calculates the viscous drag coefficient in combination with the dynamic viscosity. In practical applications, a nearly spherical oil droplet with a diameter of 100 μm in wastewater with a viscosity of 2 mPa s has a viscous drag coefficient of about 3.8x10 -6 N s / m.

[0075] According to the obtained potential distribution, the control system obtains the electric field gradient through differential calculation. The electric field gradient calculation adopts a three-dimensional finite difference method with a calculation grid size of 10 μm. The electric field gradient is multiplied by the charge amount of the oil droplet to obtain the electric field force on the oil droplet. The oil droplet charge amount is pre-determined through the electric droplet experiment method, and a typical value is 3.2x10 -15 C. In actual application cases, when the electric field gradient is 2000 V / m 2 , the electric field force on an oil droplet with a diameter of 100 μm is about 6.4x10 -12 N.

[0076] Similarly, the temperature gradient is calculated based on the temperature distribution, and the same three-dimensional finite difference method is used, but the calculation grid size is adjusted to 20 μm to adapt to the variation characteristics of the temperature field. The temperature gradient is combined with the thermophoresis coefficient of the oil droplet to calculate the thermophoretic force. The thermophoresis coefficient is pre-calibrated through the microfluidic chip, and a typical value is 0.5x10 -10 N m 2 / ℃. When the temperature gradient is 1 ℃ / mm, the thermophoretic force on an oil droplet with a diameter of 100 μm is about 5x10 -12 N.

[0077] The vector superposition of the electric field force and the thermophoretic force is realized through vector synthesis in a three-dimensional coordinate system. The control system first converts the two forces into three-dimensional vector representations, then adds the components, and finally synthesizes the oil droplet resultant force. In typical cases, when the direction of the electric field force and the thermophoretic force is 30 degrees, the magnitude of the resultant force is about 1.05x10 -11 N, and the direction deviates by 15 degrees from the electric field force.

[0078] The motion state of the oil droplet is calculated based on Newton's second law. The control system divides the oil droplet resultant force by the oil droplet mass to obtain the acceleration. The oil droplet mass is calculated based on the oil droplet volume and density, and a typical oil droplet with a diameter of 100 μm has a mass of about 5.2x10 -10kg. The oil droplet velocity and displacement were calculated by integration. When the viscous resistance was considered, the drag coefficient was incorporated into the motion equation to correct the acceleration calculation result. In practical application, the initial acceleration of the oil droplet under the action of the resultant force was about 0.02 m / s 2 , and the steady-state velocity reached was about 50 μm / s.

[0079] The determination of the initial motion state of the oil droplet includes two parts of the initial position and the initial motion parameter. The initial position is obtained by a high-speed camera system, and the spatial accuracy is ±5 μm. The initial motion parameter includes the initial velocity and the initial acceleration, which are calculated by analyzing and calculating a plurality of continuous image frames. The time resolution of the method is 2 ms, the velocity measurement accuracy is ±10 μm / s, and the acceleration measurement accuracy is ±0.005 m / s 2 . The obtained initial motion state data are stored in a database to provide a basis for subsequent demulsification control strategies.

[0080] In the embodiment, the fine scanning of the electric potential distribution is realized by arranging a microelectrode matrix on the wall surface of the flow channel, which can accurately reflect the electric field environment around the oil droplet; in combination with the real-time measurement of the temperature sensor network and the viscometer, the temperature gradient, the dynamic viscosity, and the fluid drag coefficient are dynamically characterized, so that the action of the multiple physical fields on the oil droplet is comprehensively captured. The electric field force and the thermophoretic force are vector superimposed, and in combination with the correction of the viscous resistance, the resultant force and the motion trajectory of the oil droplet can be accurately calculated, the velocity, the acceleration, and the initial position of the oil droplet are accurately determined. Therefore, not only the modeling authenticity and the calculation accuracy of the motion state of the oil droplet can be improved, but also reliable basis for predicting and controlling the migration path of the oil droplet in the subsequent oil-water separation process can be provided, the controllability and the intelligent level of the oil-water separation process are improved, and then the separation efficiency is significantly improved and the energy consumption is effectively reduced.

[0081] In an alternative embodiment, a target migration path of the oil droplet is calculated according to the initial motion state, the output size and the spatial distribution of the physical driving force are adjusted, the oil droplet is moved towards the target migration path, and the actual motion trajectory of the oil droplet is collected in real time, and the deviation between the actual motion trajectory and the target migration path is calculated, including:

[0082] A motion characteristic parameter matrix of the oil droplet is constructed based on the initial motion state, the motion characteristic parameter matrix is used to calculate the motion trajectory of the oil droplet under different physical field intensity and spatial distribution conditions, and a plurality of predicted paths with different driving force distributions are obtained;

[0083] kinematic analysis is performed on each prediction path to obtain a speed change curve and an acceleration change curve of each prediction path, driving energy consumption and time consumption of each prediction path are calculated according to the speed change curve and the acceleration change curve, an evaluation function is established with the objective of minimizing driving energy consumption and time consumption, evaluation scores of each prediction path are calculated based on the evaluation function, and a path with the highest evaluation score is selected as a target migration path;

[0084] The target migration path is divided into a plurality of continuous control intervals according to the motion characteristics of the oil droplets, and the output size and spatial distribution of the physical driving force are adjusted in each control interval to enable the oil droplets to move towards the target migration path.

[0085] The instantaneous position and speed of the oil droplets are collected in real time, an actual motion trajectory space curve is established based on time series, the actual motion trajectory space curve is projected and mapped on the target migration path in sections, and the deviation between the actual motion trajectory and the target migration path is obtained by calculating the Euclidean distance between the corresponding projection points.

[0086] Exemplarily, when constructing the motion characteristic parameter matrix of the oil droplets based on the initial motion state, the mass, size, surface charge, shape factor and other physical parameters of the oil droplets need to be organized into a multi-dimensional matrix. The matrix contains typical parameters of oil droplets with diameters ranging from 80 to 150 μm, such as mass ranging from 3.0×10 -10 to 2.1×10 -9 kg, surface charge density ranging from 1.2×10 -5 to 8.6×10 -5 C / m 2 , shape factor ranging from 0.85 to 0.98, etc. The motion characteristic parameter matrix also needs to contain initial velocity vector and acceleration vector information, and the typical initial velocity range is 10 to 100 μm / s, and the acceleration range is 0.005 to 0.05 m / s 2 . The matrix uses a fourth-order tensor structure for storage to efficiently perform subsequent calculations.

[0087] Using the constructed motion characteristic parameter matrix, the motion trajectory of the oil droplets under different physical field intensity and spatial distribution conditions can be calculated. In the calculation process, the improved finite difference method is adopted, the time step is set to 2 ms, and the spatial grid is 10 μm. The physical field intensity range is set as: electric field intensity 0.5 to 5 kV / m, gradient range 500 to 5000 V / m 2; temperature field gradient 0.2 to 2 °C / mm. Spatial distribution adopts three typical modes: uniform distribution, linear gradient distribution, and exponential gradient distribution. For each field strength and distribution combination, the predicted path is obtained by solving the motion differential equation. In actual calculation, 20 different physical field combinations are set, and 20 different predicted paths are obtained through parallel calculation. For example, when the electric field strength is 2 kV / m and the temperature gradient is 1 °C / mm, the predicted path length of an oil droplet with a diameter of 100 μm within 10 s is about 500 μm, and the trajectory is slightly curved.

[0088] When kinematic analysis is performed on each predicted path, the velocity change curve and the acceleration change curve need to be calculated. The velocity calculation adopts the central difference method, and the time interval is 5 ms; the acceleration calculation adopts the second derivative approximation, and the time interval is 10 ms. For each predicted path, the feature points include the maximum speed point, the zero acceleration point, and the turning point, and usually a 10 s predicted path contains 5-8 feature points. According to the velocity and acceleration curves, the driving energy consumption and time consumption are calculated. The driving energy consumption calculation considers the power consumption of the applied electric field (usually 0.1 to 0.5 W) and the temperature field maintenance power (usually 0.2 to 0.8 W), and multiplies the operation time to obtain the total energy consumption. The time consumption is directly determined by the total time of the oil droplet from the starting point to the ending point.

[0089] When establishing the evaluation function, the weighted summation form is adopted, in which the energy consumption weight coefficient is set to 0.6 and the time consumption weight coefficient is set to 0.4. The weight distribution can be adjusted according to the actual application requirements. The evaluation function also considers the path smoothness factor, and the smoother the path, the higher the score. For energy consumption and time consumption, normalization processing is adopted to map the numerical value to the range of 0-1. For example, the energy consumption is in the range of 0.5 to 5 J, and the time consumption is in the range of 5 to 30 s. Through the evaluation function calculation, an evaluation score is given to each predicted path, and the score range is 0 to 100. The path with the highest evaluation score is selected as the target migration path. In actual operation, when there are multiple paths with close scores (difference less than 5 points), the path with lower control complexity can be selected according to the actual control difficulty.

[0090] When the target migration path is divided into multiple continuous control intervals, the change of the oil droplet motion characteristics needs to be considered. The interval division is based on three indexes: path curvature change, velocity change rate, and physical field gradient change rate. Generally, when the curvature change exceeds 0.05 / mm, the velocity change rate exceeds 20%, or the physical field gradient change rate exceeds 15%, a new control interval needs to be set. For a typical 10 s motion path, it is usually divided into 3 to 5 control intervals. The length of each control interval ranges from 80 to 200 μm, and the time span is 1 to 3 s.

[0091] The physical driving force is adjusted within each control interval using a segmented control strategy. The electric field driving force is achieved by adjusting the voltage distribution of the microelectrode array, with a voltage adjustment range of 0.2 to 4 V, an adjustment accuracy of 0.05 V, and a response time of less than 10 ms. The temperature field driving force is achieved by adjusting the power distribution of the micro-heating element, with a power adjustment range of 0.1 to 1 W, an adjustment accuracy of 0.02 W, and a response time of less than 50 ms. For a standard oil droplet with a diameter of 100 μm, the typical electric field strength in the first control interval is set to 1.5 kV / m, and the temperature gradient is set to 0.8 ℃ / mm; in the second control interval, the electric field strength is adjusted to 2.2 kV / m, and the temperature gradient is adjusted to 1.2 ℃ / mm to ensure that the oil droplet moves along the target path.

[0092] When collecting the instantaneous position and velocity of the oil droplet in real time, a high-speed camera combined with image processing technology is used. The camera system has a frame rate of 200 fps and a spatial resolution of 2 μm / pixel. The image processing uses an improved background difference method and a contour extraction algorithm to extract the center position and boundary information of the oil droplet, with a position measurement accuracy of ±3 μm. Trajectory correlation between consecutive frames is performed using a Kalman filter algorithm, with filter parameters set as: process noise covariance matrix diagonal elements of 0.01, and measurement noise covariance matrix diagonal elements of 0.05.

[0093] When establishing the actual motion trajectory space curve based on time series, the position data obtained from consecutive frames is sorted by timestamp, and a cubic spline interpolation is used to generate a continuous curve. The node spacing of the spline interpolation is 10 ms, and the curve smoothness coefficient is set to 0.85. When projecting and mapping the actual motion trajectory and the target migration path, the nearest point projection method is used to calculate the shortest distance from each point on the actual trajectory to the target path and the corresponding projection point. The projection calculation frequency is 50 Hz, and the deviation between the actual motion trajectory and the target migration path is obtained by calculating the Euclidean distance between the corresponding projection points. The deviation value is updated in real time and used for subsequent control strategy adjustment. In practical applications, the deviation threshold is set to 15% of the diameter of the oil droplet, and when the deviation exceeds the threshold, the path correction mechanism is triggered.

[0094] In the embodiment, by establishing a motion characteristic parameter matrix and performing trajectory prediction under multiple physical field intensity and distribution conditions, a path set under different driving strategies can be obtained, and the optimal path is optimized based on a comprehensive evaluation function of energy consumption and time, so as to balance the separation efficiency and energy consumption reduction. The target migration path is decomposed into multiple continuous control intervals, and the size and distribution of the physical driving force are dynamically adjusted in the intervals, so that the oil droplets can still move stably along the predetermined path in the complex flow field. By collecting the instantaneous position and velocity of the oil droplets in real time, the actual motion trajectory is constructed and segmented projection mapping is performed with the target path, which not only can realize accurate quantification of the deviation, but also can provide a basis for subsequent control compensation, so as to significantly improve the coincidence degree of the oil droplet motion trajectory and the target path, and improve the controllability, stability and overall efficiency of the oil droplet separation process.

[0095] In an alternative embodiment, the instantaneous position and velocity of the oil droplets are collected in real time, and an actual motion trajectory space curve based on time series is established. The actual motion trajectory space curve is segmented projection mapped with the target migration path, and the deviation of the actual motion trajectory and the target migration path is obtained by calculating the Euclidean distance between the corresponding projection points, including:

[0096] The instantaneous position information of the oil droplets in the motion process is collected, and the instantaneous position information is converted into discrete space coordinate points under time series;

[0097] An initial point set is established by using the discrete space coordinate points, data smoothing is performed on the initial point set, and abnormal values are removed to obtain a corrected discrete space coordinate sequence. Based on the corrected discrete space coordinate sequence, a continuous trajectory space curve representing the actual motion of the oil droplets is generated by cubic spline interpolation fitting;

[0098] Feature control points are selected on the target migration path according to the curvature variation law, a feature control point sequence is established, and the normal projection points of each point on the continuous trajectory space curve to the target migration path are calculated. The normal projection points and the nearest feature control points establish a space mapping pairing relationship;

[0099] Based on the space mapping pairing relationship, the Euclidean distance between each pair of mapping points is calculated, the Euclidean distance is taken as the deviation value of the actual motion trajectory and the target migration path at the current position, and a complete trajectory deviation distribution is obtained.

[0100] In this embodiment, the instantaneous position information of the oil droplets during movement is collected by using a high-speed camera device to continuously capture the movement of the oil droplets. The frame rate of the camera device is set to 250 frames per second, the resolution is 1920x1080 pixels, and the field of view is 2mmx1.5mm, ensuring clear imaging of micron-sized oil droplets. The image acquisition system is equipped with a macro lens with a working distance of 10mm and a depth of field of 200μm, suitable for observing the movement of oil droplets in the flow channel. The image signal is transmitted to the image processing unit through an optical fiber, with a transmission delay of less than 5ms. During image acquisition, the flow channel background light source uses a cold light source with an intensity of 1000lux and a uniformity of more than 95%, ensuring image quality. The collected raw images undergo preprocessing steps, including grayscale conversion, contrast enhancement, and background subtraction, to enhance the contrast between the oil droplets and the background. The improved edge detection algorithm is applied to the preprocessed images to extract the oil droplet contour, with a detection accuracy of ±2μm. The center position of the oil droplet is determined by the centroid calculation method, and the pixel coordinates in the physical space are converted to actual distance coordinates through a calibration coefficient. The image processing time for each frame is less than 3ms, meeting the real-time requirement. For a standard oil droplet with a diameter of 100μm, under the condition of a flow rate of 50μm / s, a position point is recorded every 10ms, generating a set of discrete spatial coordinate points in time series.

[0101] When processing the collected discrete spatial coordinate points, an initial point set needs to be established and data smoothing and outlier rejection are required. The initial point set contains time series coordinate points on the oil droplet trajectory, with a typical data volume of 100 points per second. Data smoothing uses the sliding window method, with a window size of 7 points. The smoothing value is calculated by local weighted average. The weight coefficient adopts Gaussian distribution, with the center point weight being 0.4, and the adjacent point weights being 0.2, 0.1, 0.05 in turn. The three-sigma method is used for outlier rejection, and the standard deviation of the distance between adjacent points is calculated. When the distance between a point and the previous and next points exceeds three times the standard deviation, it is marked as an outlier. The outlier is replaced by interpolation of the previous and next points, and the interpolation method uses linear interpolation. After smoothing and outlier rejection, the corrected discrete spatial coordinate sequence is obtained. The sequence is fitted by applying the cubic spline interpolation method to generate a continuous trajectory spatial curve. The cubic spline interpolation selects the natural boundary condition to ensure the continuity of the second derivative of the curve, and the nodes are selected as the original data points. The interpolation interval is set to 0.5ms, and the continuous curve point density generated is 20 times that of the original data, ensuring the accuracy of the trajectory description. For a 10s movement process, the generated continuous trajectory spatial curve contains about 20000 points, and the curve smoothness error is less than 0.5μm.

[0102] When selecting feature control points on the target migration path, adaptive sampling is performed according to the curvature variation law. The local curvature of the target path is calculated, and the three-point method is used for curvature calculation with a point spacing of 10 μm. When the curvature variation rate exceeds 0.02 / mm, the sampling density is increased; when the curvature variation rate is less than 0.005 / mm, the sampling density is reduced. The sampling spacing in the area with large curvature is set to 5 μm, and the sampling spacing in the area with small curvature is set to 20 μm. For a typical S-shaped target path with a length of 1 mm, about 100 feature control points are selected. The feature control point sequence is stored in an ordered array, and each control point contains three-dimensional spatial coordinates and a corresponding curvature value. After establishing the feature control point sequence, the normal projection points of each point on the continuous trajectory space curve to the target migration path are calculated. The normal projection calculation uses the iterative closest point algorithm, the initial search radius is set to 50 μm, the iterative accuracy threshold is set to 0.1 μm, and the maximum number of iterations is 10. For each trajectory point, the shortest distance to the target path and the corresponding projection point coordinates are calculated. After calculating the projection points, each projection point is paired with the nearest feature control point in space. The pairing process uses the K-D tree data structure for nearest neighbor search, with a search complexity of O(log n), where n is the number of feature control points. For an oil droplet with a diameter of 100 μm, the trajectory point number is about 10,000 in a 5 s movement process, and the established spatial mapping pairing relationship is also about 10,000 pairs.

[0103] Based on the established spatial mapping pairing relationship, the Euclidean distance between each pair of mapping points is calculated. The Euclidean distance calculation considers three-dimensional spatial coordinates, calculates the distance difference in x, y, and z directions respectively, and then calculates the square root of the sum of squares. The Euclidean distance of each pair of mapping points is the deviation value of the actual motion trajectory from the target migration path at the current position. The time interval for deviation value calculation is 5 ms, which ensures that the control system can obtain trajectory deviation information in time. Statistical analysis is performed on the calculated deviation value sequence to obtain indicators such as average deviation, maximum deviation, and deviation standard deviation. In a typical physical demulsification process, the average deviation of the motion trajectory of an oil droplet with a diameter of 100 μm from the target path is about 8 μm, the maximum deviation is about 25 μm, and the deviation standard deviation is about 5 μm. The complete trajectory deviation distribution is displayed through three-dimensional visualization technology, which intuitively reflects the accuracy of oil droplet motion control. The trajectory deviation data is transmitted to the control system in real time, which is used for dynamic adjustment of subsequent physical field parameters, realizes closed-loop control, ensures that the oil droplet moves along the predetermined path, and finally realizes efficient oil-water separation.

[0104] In this embodiment, by collecting instantaneous position information and constructing time series spatial coordinate points, after smoothing and outlier rejection, cubic spline interpolation fitting is performed, which can effectively eliminate sampling noise and random disturbance, and obtain continuous and real motion trajectory curve. The introduction of feature control points based on curvature change on the target migration path can make the trajectory comparison reflect the motion difference of key positions, and improve the accuracy of mapping and matching. By establishing the projection mapping relationship between the trajectory curve and the target path, and calculating the deviation value by Euclidean distance, not only the local deviation can be obtained, but also the complete trajectory deviation distribution can be formed, so as to realize the fine monitoring and deviation evaluation of the oil droplet motion process. This method can provide accurate basis for subsequent path correction and driving force compensation, and improve the real-time performance, stability and overall efficiency of the oil droplet motion control process.

[0105] In an alternative embodiment, a compensation function of the physical field is established according to the deviation value, and the output intensity of the physical field is adjusted in combination with the motion parameters of the oil droplet, and the cooperative control mechanism of the physical field is triggered when the deviation value exceeds the preset deviation range, including:

[0106] The time differential operation is performed on the deviation value to obtain a deviation change rate, a physical field compensation function is established according to the deviation value and the deviation change rate, and a position compensation coefficient and a velocity compensation coefficient are set based on the change trend of the deviation value;

[0107] The position compensation coefficient and the velocity compensation coefficient are used to adjust the physical field compensation function, and a physical field compensation force is obtained, and the physical field compensation force is decomposed to obtain an electric field compensation force and a temperature field compensation force;

[0108] In combination with the motion parameters of the oil droplet, the output intensity of the physical field is adjusted according to the electric field compensation force and the temperature field compensation force, when the deviation value exceeds the preset deviation range, the switching timing of the physical field is determined according to the characteristic oscillation frequency of the oil droplet, the output intensity of the electric field and the temperature field is alternately adjusted according to the switching timing, and the cooperative control mechanism of the physical field is triggered.

[0109] Exemplarily, when the deviation value is time-differentiated, the central difference method is used to calculate the deviation rate of change. The deviation values of adjacent time points are taken to calculate the change amount of the deviation in unit time, and the deviation rate of change is obtained. The time interval is set to 5 ms to ensure the balance between the calculation accuracy and the system response speed. For an oil droplet with a diameter of 100 μm, the typical deviation value range is 0 to 30 μm, and the deviation rate of change range is -5 to 5 μm / s. When the physical field compensation function is established according to the deviation value and the deviation rate of change, the proportional-differential control strategy is used. The basic form of the compensation function is the deviation value × position compensation coefficient + deviation rate of change × velocity compensation coefficient. For oil droplets of different sizes, the position compensation coefficient and the velocity compensation coefficient need to be dynamically adjusted. The position compensation coefficient is inversely proportional to the oil droplet diameter, and for an oil droplet with a diameter of 100 μm, the typical value of the position compensation coefficient is 0.02 N / μm; the velocity compensation coefficient is proportional to the mass of the oil droplet, and for an oil droplet with a mass of 5.2 × 10 -10 kg, the typical value of the velocity compensation coefficient is 0.008 N·s / μm.

[0110] When the compensation coefficients are set based on the change trend of the deviation value, an adaptive adjustment mechanism is introduced. When the deviation value shows an increasing trend for three consecutive sampling periods, the position compensation coefficient increases by 15%, and the velocity compensation coefficient increases by 10%; when the deviation value shows a decreasing trend for three consecutive sampling periods, the position compensation coefficient decreases by 8%, and the velocity compensation coefficient decreases by 5%. The adjustment range of the compensation coefficients has upper and lower limits, the range of the position compensation coefficient is 0.01 to 0.05 N / μm, and the range of the velocity compensation coefficient is 0.004 to 0.015 N·s / μm. For an oil droplet with a diameter of 100 μm, when the deviation value increases from 5 μm to 15 μm, the position compensation coefficient is adjusted from 0.02 N / μm to 0.023 N / μm, and the velocity compensation coefficient is adjusted from 0.008 N·s / μm to 0.0088 N·s / μm.

[0111] When the physical field compensation force is calculated using the adjusted position compensation coefficient and velocity compensation coefficient, the deviation value is multiplied by the position compensation coefficient to obtain the position compensation component, the deviation rate of change is multiplied by the velocity compensation coefficient to obtain the velocity compensation component, and the two are added to obtain the physical field compensation force. When the deviation value is 10 μm and the deviation rate of change is 2 μm / s, the calculated physical field compensation force is about 2.16 × 10 -10 N. When the physical field compensation force is decomposed into the electric field compensation force and the temperature field compensation force, the response characteristics of the oil droplet to different physical fields are considered. For a charged oil droplet, the electric field response sensitivity is high, and the temperature field response is relatively slow but long-lasting. Therefore, the decomposition ratio of the physical field compensation force is dynamically adjusted according to the oil droplet characteristics and control requirements. The typical decomposition ratio is: the electric field compensation force accounts for 70%, and the temperature field compensation force accounts for 30%. For the physical field compensation force calculated above, the electric field compensation force is about 1.51 × 10 -10N, the temperature field compensation force is about 6.48 x 10 -11 N.

[0112] When adjusting the output intensity of the physical field in combination with the motion parameters of the oil droplets, factors such as the velocity, acceleration, and dynamic viscosity of the oil droplets need to be considered. The greater the velocity of the oil droplets, the shorter the response time of the physical field adjustment needs to be; the greater the acceleration of the oil droplets, the higher the rate of change of the physical field intensity needs to be; and the greater the dynamic viscosity of the waste water, the stronger the physical field intensity needs to be. When adjusting the electric field intensity, the required change in electric field intensity is calculated according to the electric field compensation force and the charge amount of the oil droplets. For oil droplets with a charge amount of 3.2 x 10 -15 C, the electric field compensation force is 1.51 x 10 -10 N, the electric field intensity needs to be increased by about 472 V / m. The adjustment of the electric field intensity is achieved by changing the voltage on the microelectrode array, with a voltage adjustment accuracy of 0.02 V and a response time of less than 5 ms. The adjustment of the temperature field intensity is achieved by controlling the power of the micro-heating element, and the required change in temperature gradient is calculated according to the temperature field compensation force. For oil droplets with a thermophoresis coefficient of 0.5 x 10 2 / ℃, the temperature field compensation force is 6.48 x 10 -11 N, the temperature gradient needs to be increased by about 0.13 ℃ / mm. The power adjustment accuracy of the micro-heating element is 0.01 W, and the response time is less than 20 ms.

[0113] When the deviation value exceeds the preset deviation range, the cooperative control mechanism of the physical field is triggered. The preset deviation range is usually set to 20% of the diameter of the oil droplets, and for oil droplets with a diameter of 100 μm, the preset deviation range is 20 μm. The core of the cooperative control mechanism is to determine the switching timing of the physical field according to the characteristic oscillation frequency of the oil droplets. The characteristic oscillation frequency of the oil droplets in the fluid is related to their diameter, density, and dynamic viscosity of the surrounding fluid. For oil droplets with a diameter of 100 μm and a density of 850 kg / m 3 , the characteristic oscillation frequency is about 8 Hz in waste water with a dynamic viscosity of 2 mPa·s. The switching timing is set to 1.5 times the characteristic oscillation period, i.e. about 187.5 ms. The switching of the physical field uses an alternating control strategy, and the output intensity of the electric field and the temperature field is adjusted alternately according to the switching timing. Specifically, in one switching period, the electric field intensity is first increased to the maximum value, and then decreased after maintaining for half a period; at the same time, the temperature field intensity starts from a low value and increases to the maximum value when the electric field intensity decreases. The resultant force generated by this alternating adjustment causes the oil droplets to produce controlled oscillation, effectively reducing the deviation and restoring the predetermined trajectory.

[0114] The implementation of the synergistic control mechanism requires precise timing control. The control system uses a high-precision timer with a time resolution of 1 ms to ensure accurate switching of the electric field and temperature field. In the above case, during the synergistic control process, the electric field strength varies from 1 kV / m to 3 kV / m, and the temperature gradient varies from 0.5°C / mm to 1.5°C / mm. The rise time of the electric field strength is 30 ms, and the fall time is 50 ms; the rise time of the temperature gradient is 60 ms, and the fall time is 80 ms. Through this synergistic control mechanism, when the oil droplet deviates from the predetermined path by 22 μm, after 3 switching cycles (about 562.5 ms) of adjustment, the deviation can be reduced to within 8 μm, achieving effective correction of the oil droplet trajectory. The synergistic control mechanism not only improves the control accuracy but also enhances the system's resistance to external disturbances, providing a reliable guarantee for efficient oil-water separation.

[0115] In this embodiment, by establishing a physical field compensation function for the deviation value and its rate of change, and introducing position and velocity compensation coefficients, continuous correction of the oil droplet motion state can be achieved, allowing it to move stably along the target path. When the deviation exceeds the preset range, combined with the oil droplet motion parameters and characteristic oscillation frequency, the electric field and temperature field outputs are alternately adjusted according to the optimized switching timing to trigger the synergistic control mechanism of the physical fields, effectively suppressing the tendency of the oil droplet to deviate from the target path. This method not only improves the degree of agreement between the oil droplet trajectory and the predetermined path, achieving high-precision and high-stability motion control, but also optimizes the driving efficiency of the physical fields, reduces energy consumption, and enhances the intelligent level and overall separation efficiency of the oil-water separation process, ensuring real-time response capability and operational reliability of the system in complex fluid environments.

[0116] As shown in Figure 2 , the oil-water separation multi-physical field synergistic control process of the present embodiment is shown.

[0117] In an alternative embodiment, the instantaneous motion state and coalescence degree of the oil droplet are collected, and based on the compensation function and synergistic control mechanism of the physical fields, the energy input sequence of each physical field is calculated to determine the optimal timing ratio between the physical fields, including:

[0118] The oil droplet image is acquired, and the instantaneous motion state of the oil droplet is reconstructed through image depth information, including the spatial position coordinates, motion velocity, and acceleration of the oil droplet. Meanwhile, edge extraction is performed on the oil droplet image, and the local curvature distribution of the oil droplet interface is calculated. According to the change amount of the interface curvature at adjacent time points and the curvature change trend of the oil droplet motion trajectory, the instantaneous coalescence degree of the oil droplet is obtained;

[0119] According to the instantaneous motion state and instantaneous coalescence degree, the compensation coefficient is determined in the preset physical field compensation function, and the synergistic control mechanism of the physical fields is triggered. The compensation amount of each physical field is calculated based on the physical field compensation function;

[0120] The instantaneous motion state change and instantaneous coalescence degree change of the oil droplets under the compensation amount of each physical field are calculated, the energy conversion efficiency of each physical field is calculated, and the energy input sequence of the electric field, the acoustic field and the temperature field is calculated based on the energy conversion efficiency and the change trend of the instantaneous coalescence degree;

[0121] The difference between the maximum value and the minimum value of each physical field energy sequence in the energy input sequence is calculated to obtain the energy fluctuation amplitude of the physical field, and the optimal time sequence ratio of the physical field is determined according to the order from small to large of the energy fluctuation amplitude.

[0122] In this embodiment, a double-camera stereo imaging system is used to acquire oil droplet images, with a resolution of 2048x1536 pixels, a frame rate of 120Hz, a field of view of 30°, and a depth of field of 2mm. The baseline distance between the cameras is 15mm, and the depth information is calculated by a binocular disparity algorithm. The generation of the depth map uses a semi-global algorithm based on local matching, with a matching window size of 11x11 pixels and a disparity search range of 0-64 pixels. The depth measurement accuracy reaches ±5μm. After the depth map is fused with the original image, a three-dimensional reconstruction algorithm is applied to obtain the spatial position coordinates of the oil droplets. The spatial coordinates are calculated by time difference of consecutive multiple images to obtain the motion velocity, and then the velocity is calculated by time difference to obtain the acceleration. For oil droplets with a diameter of 100μm, under the condition of a flow rate of 200μm / s, the position measurement accuracy is ±3μm, the velocity measurement accuracy is ±8μm / s, and the acceleration measurement accuracy is ±0.02m / s 2 .

[0123] The edge of the oil droplet image is extracted using the Canny algorithm, with the high threshold set to 30% of the maximum gray value of the image and the low threshold set to 40% of the high threshold. The size of the Gaussian smoothing kernel is 5x5 pixels. The extracted edge is fitted by B-spline to generate a continuous oil droplet contour curve, with a control point spacing of 5μm. The local curvature is calculated along the contour curve, using a three-point circle fitting method with a neighboring point spacing of 2μm. For standard spherical oil droplets, the curvature distribution is uniform, with a standard deviation less than 0.001μm -1 ; for coalesced oil droplets, the curvature distribution is non-uniform, with a significant change in local curvature, and the ratio of maximum curvature to minimum curvature can reach 10:1. The change amount of the interface curvature at adjacent moments is calculated by difference, and for oil droplets in the coalescence process, the typical curvature change amount is 0.02-0.05μm -1 / ms. Combined with the curvature change trend of the oil droplet motion trajectory, the instantaneous coalescence degree of the oil droplet is calculated by weighted summation. The coalescence degree index ranges from 0 to 1, with 0 indicating no coalescence and 1 indicating complete coalescence. In a typical coalescence process, the time for the coalescence degree to increase from 0.1 to 0.9 is about 50-200ms, depending on the oil droplet size and physical field strength.

[0124] The compensation coefficients of the physical field compensation function are determined based on the instantaneous motion state and the instantaneous coalescence degree. A multi-layer perceptron neural network is used for mapping. The input layer of the network contains 6 nodes corresponding to the three-dimensional position coordinates, velocity, acceleration and coalescence degree of the oil droplets. The hidden layer contains two layers with 12 nodes in each layer, and the activation function is ReLU. The output layer contains 3 nodes corresponding to the compensation coefficients of the electric field, acoustic field and temperature field. The network is obtained through offline training, and the training data set contains 5000 groups of corresponding relationships between the motion and coalescence state of the oil droplets and the optimal compensation coefficients. The preset physical field compensation function adopts a quadratic polynomial form, including position terms, velocity terms and coalescence degree terms, and the weight coefficients between the terms are determined by the compensation coefficients output by the above neural network. When the coalescence degree exceeds 0.5, the compensation coefficients of the physical fields are increased by 50% to accelerate the coalescence process. For an oil droplet with a diameter of 100 μm and a coalescence degree of 0.4, the typical values of the electric field compensation coefficient are 0.015 N / μm, the acoustic field compensation coefficient is 0.008 N / μm, and the temperature field compensation coefficient is 0.012 N / μm.

[0125] When calculating the compensation amount of each physical field based on the physical field compensation function, the compensation coefficients are multiplied by the deviation value and the deviation change rate to obtain the compensation force of each physical field. The compensation force is then divided by the response coefficient of the corresponding physical field of the oil droplet to obtain the compensation amount of each physical field. For the above case, the calculated electric field compensation amount is 300 V / m, the acoustic field compensation amount is 0.15 MPa, and the temperature field compensation amount is 0.24 ℃ / mm. The compensation amount of the physical field is converted into specific execution instructions by the control system. The electric field compensation is achieved by adjusting the voltage of the microelectrode array, the acoustic field compensation is achieved by adjusting the driving voltage of the piezoelectric transducer, and the temperature field compensation is achieved by adjusting the power of the micro-heating element.

[0126] Under the action of the compensation amount of each physical field, the instantaneous motion state and the coalescence degree of the oil droplets will change. By comparing the change amount before and after compensation, the energy conversion efficiency of each physical field is calculated. The energy conversion efficiency is equal to the ratio of the change amount of the kinetic energy and the surface energy of the oil droplets to the input energy of the physical field. For the electric field, the input energy is calculated as the square of the electric field strength multiplied by the dielectric constant and then multiplied by the volume of the oil droplet; for the acoustic field, the input energy is calculated as the square of the sound pressure divided by the product of the medium density and the sound speed and then multiplied by the volume of the oil droplet; for the temperature field, the input energy is calculated as the square of the temperature gradient multiplied by the thermal conductivity and then multiplied by the volume of the oil droplet. The typical energy conversion efficiency is: electric field 40-60%, acoustic field 30-50%, temperature field 25-45%, which depends on the physical properties of the oil droplets and the environmental conditions.

[0127] Based on the trends of energy conversion efficiency and coalescence degree, a dynamic programming algorithm is used to calculate the energy input sequence of each physical field. The time window is set to 500 ms, divided into 10 time periods, each time period is 50 ms. For each time period, based on the current state and energy conversion efficiency, the optimal energy allocation scheme is calculated. The constraint conditions of energy allocation are that the total energy input does not exceed the preset value, and the energy input of each physical field is not less than the minimum threshold. For the stage where the coalescence degree rises rapidly, increase the input of the physical field with high energy efficiency; for the stage where the coalescence degree changes slowly, balance the energy input of each physical field. The calculated energy input sequence forms a 3x10 matrix, each row represents a physical field, and each column represents the energy input value of a time period.

[0128] The difference between the maximum and minimum values of each physical field energy sequence in the energy input sequence is calculated to obtain the energy fluctuation amplitude. For the above calculated energy input sequence, the energy fluctuation amplitude of the electric field is 0.08 J, the energy fluctuation amplitude of the acoustic field is 0.12 J, and the energy fluctuation amplitude of the temperature field is 0.05 J. According to the order of energy fluctuation amplitude from small to large, the physical fields are arranged in the order of: temperature field, electric field, acoustic field. The optimal time sequence ratio of the physical field is determined according to the proportion of the energy fluctuation amplitude, and the time sequence ratio of the temperature field: electric field: acoustic field is calculated to be 1:1.6:2.4. This means that in the process of collaborative control, the duration of the temperature field is the longest, and the duration of the acoustic field is the shortest but the intensity is the highest. The optimal time sequence ratio is applied to the switching control of the physical field, which ensures that each physical field plays the maximum effect at the most suitable time point, and realizes the efficient coalescence and separation of oil droplets. Through this method, the oil-water separation efficiency can be improved by 30-50%, the processing time can be shortened by 40-60%, and the energy consumption can be reduced by 25-45%, providing an efficient solution for industrial wastewater treatment.

[0129] In this embodiment, by real-time acquisition of the instantaneous motion state and coalescence degree of oil droplets, the system can dynamically adjust the physical field parameters, so that the oil droplets move according to the preset trajectory and accelerate the coalescence process. A multi-physical field collaborative control mechanism is adopted, and the optimal energy input sequence is calculated according to the energy conversion efficiency, to ensure that each physical field plays the maximum effect at the best time. The optimal time sequence ratio determined by the energy fluctuation amplitude sorting method can significantly improve the energy utilization efficiency and reduce energy waste. This scheme realizes high-precision control of the oil droplet motion trajectory and efficient promotion of the coalescence process, and can adaptively adjust the control strategy for oil-containing wastewater with different properties, ensuring stable and reliable oil-water separation effect under various working conditions.

[0130] In an alternative embodiment, the output power of each physical field is adjusted according to the optimal time sequence ratio, the coalescence size of the oil droplets is monitored in real time, and when the coalescence size reaches the preset value, the physical fields are gradually turned off according to the energy input sequence to complete the oil-water separation process, including:

[0131] According to the optimal time sequence ratio, the output power of the physical field is adjusted to the value corresponding to the energy input sequence; the output power of the electric field, the sound field and the temperature field is adjusted according to the proportion of the energy input sequence, and the adjustment sequence of the power of each physical field is controlled based on the optimal time sequence ratio;

[0132] The coalescence size of the oil droplets is monitored in real time and time window average processing is performed to obtain the average coalescence size of the oil droplets, and when the average coalescence size of the oil droplets reaches a preset size value, the physical field is closed according to the size order of the energy fluctuation amplitude in the optimal time sequence ratio;

[0133] The average coalescence size of the oil droplets is monitored in the closing time interval of the adjacent physical field, and when the average coalescence size of the oil droplets exceeds a preset size range, the closed physical field is started, and the power is adjusted to a set proportion of the value corresponding to the energy input sequence, the average coalescence size of the oil droplets is monitored, and when the average coalescence size of the oil droplets is within the preset size range and lasts for a preset time, the oil-water separation process is completed.

[0134] In this embodiment, when the output power of the physical field is adjusted to the value corresponding to the energy input sequence according to the optimal time sequence ratio, a hierarchical control strategy needs to be adopted. For the electric field, the output voltage is adjusted by a high-precision power supply module controlled by a microprocessor, the voltage adjustment range is 0.5-10kV, the adjustment accuracy is 0.1kV, and the response time is less than 5ms. The electric field power is calculated as the square of the voltage divided by the resistance of the medium between the electrodes, and the power range is 0.1-5W. For the sound field, a piezoelectric ceramic transducer is used to generate ultrasonic waves, the frequency range is 20-100kHz, and the sound intensity range is 0.1-2W / cm 2 The power is adjusted by adjusting the driving voltage of the transducer, the voltage range is 5-50V, the adjustment accuracy is 0.5V, and the response time is less than 10ms. For the temperature field, a micro-heating element array is used to generate a temperature gradient, the power range is 0.2-3W, the on-time proportion of the heating element is controlled by pulse width modulation, the modulation frequency is 1kHz, the duty cycle adjustment range is 10%-90%, the adjustment accuracy is 1%, and the response time is less than 20ms.

[0135] When adjusting the output power of each physical field according to the proportion of the energy input sequence, the mutual influence between the physical fields needs to be considered. Assuming that the energy proportion of the electric field, the acoustic field and the temperature field at a certain time in the energy input sequence is 2:1:1.5, the output power of each physical field needs to be set according to this proportion. For the case that the total power is 4W, the electric field power is set to 2W, the acoustic field power is set to 1W, and the temperature field power is set to 1.5W. The adjustment sequence of the power of each physical field is based on the optimal time sequence ratio, for example, when the time sequence ratio is temperature field:electric field:acoustic field=1:1.6:2.4, the adjustment sequence is temperature field first, then electric field, and finally acoustic field. In the adjustment process, a gradual control strategy is adopted, and the power change rate is limited within 20% / s to avoid system instability caused by sudden changes.

[0136] Real-time monitoring of the coalescence size of oil droplets adopts a high-speed image processing technology. The image acquisition system includes a high-speed camera with a frame rate of 200fps and a resolution of 1280x1024 pixels, equipped with a macro lens with a field of view of 2x1.6mm and a pixel corresponding to an actual size of 1.56μm / pixel. The image processing algorithm adopts an improved edge detection and region growing method, the edge detection adopts Canny algorithm, the low threshold is 50, the high threshold is 150, and the Gaussian kernel size is 5x5 pixels. The initial seed point of the region growing method is selected as the center region of the image, and the growth threshold is the gray difference value 10. The oil droplet region is determined by connected component analysis, and the equivalent diameter is calculated as the coalescence size. In order to eliminate the influence of instantaneous fluctuations, the coalescence size is processed by time window averaging, the window size is 20 frames (i.e. 100ms), the weighted moving average method is adopted, and the weight coefficient is exponentially attenuated, the latest frame weight is 0.2, and the weight of the previous frames is 0.16, 0.128, etc. After time window averaging, the average coalescence size of the oil droplets is obtained, and the measurement accuracy reaches ±3μm.

[0137] When the average coalescence size of the oil droplets reaches the preset size value, the physical fields are closed according to the size order of the energy fluctuation amplitude in the optimal time sequence ratio. The preset size value is usually set as the target separation particle size, such as 300μm. When the size order of the energy fluctuation amplitude is acoustic field>electric field>temperature field, the acoustic field is closed first, then the electric field is closed, and finally the temperature field is closed. The physical field is closed in a gradient descent manner, and the power is linearly reduced to zero within 200ms to avoid pressure fluctuations caused by sudden closing. The closing time interval of adjacent physical fields is set to 500ms, and the change trend of the average coalescence size of the oil droplets is monitored within this interval.

[0138] The average coalesced size of oil droplets is monitored during the off-time interval of the adjacent physical field. When the average coalesced size of oil droplets exceeds the preset size range, the closed physical field is started. The preset size range is usually set to ±10% of the target separation particle size, such as 270-330 μm. When the average coalesced size of oil droplets is detected to be less than 270 μm, it indicates that the coalescence is insufficient, and the recently closed physical field needs to be restarted. When starting, the power is adjusted to a set proportion of the corresponding value of the energy input sequence, which is usually set to 70% to avoid too strong physical field causing the coalesced oil droplets to be dispersed again. For example, if the electric field power before closing is 2 W, it is set to 1.4 W when restarting. After starting, the average coalesced size of oil droplets is continuously monitored. When it is within the preset size range and lasts for a preset time (usually 2 s), it is determined that the coalesced state of oil droplets is stable, and the physical field can be closed again.

[0139] Through the above process cycle control, all physical fields are finally closed, and the average coalesced size of oil droplets remains within the preset size range, i.e. the oil-water separation process is completed. During the entire process, the control system also needs to record the power curve of each physical field and the change curve of the average coalesced size of oil droplets, which is used for subsequent process optimization. For simulated wastewater with an oil concentration of 5000 mg / L, the initial particle size of oil droplets is 50 μm. After the synergistic effect of physical fields, it is coalesced to 300 μm within 30 s, and the total energy consumption is about 120 J, which is about 40% lower than that of the traditional method, and the processing time is shortened by about 50%.

[0140] To adapt to different types of oil-containing wastewater, the method also uses an adaptive parameter adjustment mechanism. According to the initial oil droplet particle size distribution and oil concentration, the preset size value and the preset size range are dynamically adjusted. For high-concentration wastewater (>8000 mg / L), the preset size can be appropriately increased to 350-400 μm; for low-concentration wastewater (<2000 mg / L), the preset size can be reduced to 200-250 μm. At the same time, according to the coalescence rate of oil droplets, the off-time interval of the physical field is dynamically adjusted. The interval is shortened when the coalescence rate is fast, and the interval is lengthened when the coalescence rate is slow, to ensure the efficiency and stability of the process. Through this intelligent control strategy, the method can adapt to the oil-water separation requirements under various working conditions, achieving the dual optimization of energy efficient utilization and separation effect.

[0141] In this embodiment, by adjusting the output power of the electric field, the acoustic field and the temperature field according to the optimal timing ratio, and accurately controlling the adjustment sequence of each physical field according to the energy input sequence, the precise driving of the oil droplet coalescence process can be realized. Real-time monitoring of the oil droplet coalescence size and time window average processing enable the system to dynamically judge the oil droplet coalescence state, and when the preset size is reached, each physical field is gradually closed to avoid excessive energy input and improve energy efficiency. At the same time, during the process of closing the physical field, the oil droplet coalescence size is continuously monitored, and if necessary, the corresponding physical field is restarted to maintain the size within the preset range, thereby ensuring the stability and separation effect of the oil droplet coalescence process. This method not only improves the oil-water separation efficiency and control accuracy, but also optimizes energy consumption, realizing automatic and intelligent management of the whole process.

[0142] In a second aspect of the embodiments of the present application, an efficient oil-water separation intelligent control system based on physical demulsification is provided, which comprises:

[0143] A first unit for detecting the physical parameters of oil droplets in oil-containing wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets;

[0144] A second unit for calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force to make the oil droplets move towards the target migration path, and real-time collecting the actual motion trajectory of the oil droplets and calculating the deviation value between the actual motion trajectory and the target migration path;

[0145] A third unit for establishing a compensation function of the physical field according to the deviation value, adjusting the output intensity of the physical field combined with the motion parameters of the oil droplets, and triggering the cooperative control mechanism of the physical field when the deviation value exceeds the preset deviation range;

[0146] A fourth unit for collecting the instantaneous motion state and coalescence degree of the oil droplets, calculating the energy input sequence of each physical field based on the compensation function of the physical field and the cooperative control mechanism, and determining the optimal timing ratio between the physical fields;

[0147] A fifth unit for adjusting the output power of each physical field according to the optimal timing ratio, real-time monitoring the coalescence size of the oil droplets, and gradually closing the physical field according to the energy input sequence when the coalescence size reaches the preset value to complete the oil-water separation process.

[0148] In a third aspect of the embodiments of the present application, an electronic device is provided, which comprises:

[0149] A processor;

[0150] A memory for storing processor-executable instructions;

[0151] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0152] In a fourth aspect, the present application provides a computer readable storage medium, having stored thereon computer program instructions, which when executed by a processor, implement the method described above.

[0153] The present application can be a method, an apparatus, a system, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for performing various aspects of the present application.

[0154] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application; although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that: it can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An efficient oil-water separation intelligent control method based on physical demulsification, characterized in that, The method comprises the following steps: detecting the physical parameters of oil droplets in oily wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets; calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force, making the oil droplets move towards the target migration path, and collecting the actual motion trajectory of the oil droplets in real time, and calculating the deviation value between the actual motion trajectory and the target migration path; establishing a compensation function of the physical field according to the deviation value, adjusting the output intensity of the physical field combined with the motion parameters of the oil droplets, and triggering the cooperative control mechanism of the physical field when the deviation value exceeds the preset deviation range; collecting the instantaneous motion state and coalescence degree of the oil droplets, calculating the energy input sequence of each physical field based on the compensation function of the physical field and the cooperative control mechanism, and determining the optimal timing ratio between the physical fields; adjusting the output power of each physical field according to the optimal timing ratio, and monitoring the coalescence size of the oil droplets in real time, and when the coalescence size reaches the preset value, gradually closing the physical fields according to the energy input sequence to complete the oil-water separation process.

2. The method of claim 1, wherein, The method for detecting the physical parameters of oil droplets in oily wastewater, calculating the distribution gradient of each physical field, and obtaining the initial motion state of the oil droplets comprises the following steps: arranging a microelectrode matrix on the wall surface of the flow channel, performing potential scanning on the surface of the oil droplets in the oily wastewater through the microelectrode matrix, and obtaining the potential distribution around the oil droplets; setting a temperature sensor network in the flow channel, obtaining the temperature distribution around the oil droplets, and measuring the dynamic viscosity of the oily wastewater through a viscometer, and calculating the viscous resistance coefficient in the flow channel according to the dynamic viscosity and the shape parameters of the oil droplets; calculating the electric field gradient around the oil droplets and the electric field force acting on the oil droplets according to the potential distribution, calculating the temperature gradient around the oil droplets and the thermophoresis force acting on the oil droplets according to the temperature distribution, and superimposing the electric field force and the thermophoresis force to obtain the resultant force of the oil droplets; calculating the force size and direction acting on the oil droplets according to the resultant force of the oil droplets and the viscous resistance coefficient, obtaining the motion speed and acceleration of the oil droplets, and determining the initial motion state of the oil droplets, wherein the initial motion state includes the initial position and initial motion parameters of the oil droplets.

3. The method of claim 1, wherein, The method for calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force, making the oil droplets move towards the target migration path, and collecting the actual motion trajectory of the oil droplets in real time, and calculating the deviation between the actual motion trajectory and the target migration path comprises the following steps: constructing a motion characteristic parameter matrix of the oil droplets based on the initial motion state, calculating the motion trajectory of the oil droplets under different physical field intensity and spatial distribution conditions by using the motion characteristic parameter matrix, and obtaining multiple predicted paths with different driving force distributions; performing kinematic analysis on each predicted path to obtain the speed change curve and acceleration change curve of each predicted path, calculating the driving energy consumption and time consumption of each predicted path according to the speed change curve and acceleration change curve, establishing an evaluation function with the minimum driving energy consumption and time consumption as the target, calculating the evaluation score of each predicted path based on the evaluation function, and selecting the path with the highest evaluation score as the target migration path; The target migration path is divided into multiple continuous control intervals according to the movement characteristics of the oil droplets, and the output size and spatial distribution of the physical driving force are adjusted in each control interval to make the oil droplets move towards the target migration path. The instantaneous position and velocity of the oil droplets are collected in real time, and an actual motion trajectory space curve based on time series is established. The actual motion trajectory space curve is projected and mapped on the target migration path in sections, and the deviation between the corresponding projection points is calculated to obtain the deviation between the actual motion trajectory and the target migration path.

4. The method of claim 1, wherein, The instantaneous position and velocity of the oil droplets are collected in real time, and an actual motion trajectory space curve based on time series is established. The actual motion trajectory space curve is projected and mapped on the target migration path in sections, and the deviation between the corresponding projection points is calculated to obtain the deviation between the actual motion trajectory and the target migration path. The instantaneous position information of the oil droplets during the movement process is collected, and the instantaneous position information is converted into discrete spatial coordinate points under time series. An initial point set is established using the discrete spatial coordinate points, data smoothing is performed on the initial point set, and abnormal values are removed to obtain a corrected discrete spatial coordinate sequence. A continuous trajectory space curve representing the actual motion of the oil droplets is generated by cubic spline interpolation fitting based on the corrected discrete spatial coordinate sequence. Characteristic control points are selected on the target migration path according to the curvature variation law, a sequence of characteristic control points is established, the normal projection points of each point on the continuous trajectory space curve to the target migration path are calculated, and the normal projection points are spatially mapped and paired with the nearest characteristic control points. Based on the spatial mapping and pairing relationship, the Euclidean distance between each pair of mapping points is calculated, the Euclidean distance is taken as the deviation value of the actual motion trajectory at the current position from the target migration path, and a complete trajectory deviation distribution is obtained.

5. The method of claim 1, wherein, A compensation function of the physical field is established according to the deviation value, the output intensity of the physical field is adjusted in combination with the motion parameters of the oil droplets, and the collaborative control mechanism of the physical field is triggered when the deviation value exceeds the preset deviation range, including: The deviation value is subjected to time differentiation to obtain a deviation change rate, a physical field compensation function is established according to the deviation value and the deviation change rate, and a position compensation coefficient and a velocity compensation coefficient are set based on the variation trend of the deviation value; The physical field compensation function is adjusted using the position compensation coefficient and the velocity compensation coefficient to obtain a physical field compensation force, and the physical field compensation force is decomposed to obtain an electric field compensation force and a temperature field compensation force; In combination with the motion parameters of the oil droplets, the output intensity of the physical field is adjusted according to the electric field compensation force and the temperature field compensation force, when the deviation value exceeds the preset deviation range, the switching timing of the physical field is determined according to the characteristic oscillation frequency of the oil droplets, the output intensity of the electric field and the temperature field is alternately adjusted according to the switching timing, and the collaborative control mechanism of the physical field is triggered.

6. The method of claim 1, wherein, The instantaneous motion state and coalescence degree of the oil droplets are collected, the energy input sequence of each physical field is calculated based on the compensation function and the collaborative control mechanism of the physical field, and the optimal timing ratio between the physical fields is determined, including: The oil droplet image is acquired, the instantaneous motion state of the oil droplet is reconstructed through the depth information of the image, including the spatial position coordinates, the motion speed and the acceleration of the oil droplet, the edge of the oil droplet image is extracted, the local curvature distribution of the interface of the oil droplet is calculated, and the instantaneous coalescence degree of the oil droplet is acquired according to the change amount of the interface curvature of adjacent time instants and the curvature change trend of the motion trajectory of the oil droplet; According to the instantaneous motion state and the instantaneous coalescence degree, a compensation coefficient is determined in a preset physical field compensation function, and a cooperative control mechanism of the physical field is triggered, and a compensation amount of each physical field is calculated based on the physical field compensation function; Under the action of the compensation amount of each physical field, the change of the instantaneous motion state of the oil droplet and the change of the instantaneous coalescence degree are calculated, the energy conversion efficiency of each physical field is calculated, and based on the energy conversion efficiency, the energy input sequence of the electric field, the acoustic field and the temperature field is calculated in combination with the change trend of the instantaneous coalescence degree; The difference between the maximum value and the minimum value of each physical field energy sequence in the energy input sequence is calculated to obtain the energy fluctuation amplitude of the physical field, and the optimal time sequence matching of the physical field is determined according to the order from small to large of the energy fluctuation amplitude.

7. The method of claim 1, wherein, The output power of each physical field is adjusted according to the optimal time sequence matching, the coalescence size of the oil droplet is monitored in real time, and when the coalescence size reaches a preset value, the physical field is gradually closed according to the energy input sequence to complete the oil-water separation process, including: The output power of the physical field is adjusted to the value corresponding to the energy input sequence according to the optimal time sequence matching; the output power of the electric field, the acoustic field and the temperature field is adjusted according to the proportion of the energy input sequence, and the adjustment order of the power of each physical field is controlled based on the optimal time sequence matching; The coalescence size of the oil droplet is monitored in real time and is processed by time window averaging to obtain the average coalescence size of the oil droplet, and when the average coalescence size of the oil droplet reaches a preset size value, the physical field is closed according to the order of the energy fluctuation amplitude in the optimal time sequence matching; The average coalescence size of the oil droplet is monitored within the closing time interval of adjacent physical fields, and when the average coalescence size of the oil droplet exceeds a preset size range, the closed physical field is started and the power is adjusted to a set proportion of the value corresponding to the energy input sequence, and the average coalescence size of the oil droplet is monitored, and when the average coalescence size of the oil droplet is within the preset size range and lasts for a preset time, the oil-water separation process is completed.

8. An intelligent control system for efficient oil-water separation based on physical demulsification for implementing the method according to any one of the preceding claims 1-7, characterized in that, including: The first unit is used for detecting the physical parameters of the oil droplets in the oily wastewater, calculating the distribution gradient of each physical field, and acquiring the initial motion state of the oil droplets; The second unit is used for calculating the target migration path of the oil droplets according to the initial motion state, adjusting the output size and spatial distribution of the physical driving force, making the oil droplets move towards the target migration path, and collecting the actual motion trajectory of the oil droplets in real time to calculate the deviation value between the actual motion trajectory and the target migration path; The third unit is used for establishing a compensation function of the physical field according to the deviation value, adjusting the output intensity of the physical field in combination with the motion parameters of the oil droplets, and triggering the cooperative control mechanism of the physical field when the deviation value exceeds a preset deviation range; The fourth unit is used for acquiring the instantaneous motion state and the coalescence degree of the oil droplets, calculating the energy input sequence of each physical field based on the compensation function and the cooperative control mechanism of the physical field, and determining the optimal time sequence matching between the physical fields. The fifth unit is used for adjusting the output power of each physical field according to the optimal timing ratio, monitoring the size of the coalesced oil droplets in real time, and gradually shutting down the physical fields according to the energy input sequence when the size of the coalesced oil droplets reaches a preset value, so as to complete the oil-water separation process.

9. An electronic device, comprising: Comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method of any one of claims 1 to 7.

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