An online feed production management method and system

Abstract

The application discloses a kind of feed online production management method and system, for improving the uniformity of trace element electrostatic distribution in the mixing process of feed online production;The method comprises evenly arranging electrostatic field intensity sensor and ion generator on the inner wall of mixing machine, and the electrostatic field intensity distribution of each monitoring point in the mixing process is monitored in real time;Based on the physical relationship between electrostatic field distribution and trace element migration, a trace element concentration distribution deviation model is established;The optimal output current intensity of each ion generator is calculated using a multi-objective optimization algorithm, and the correction of trace element distribution deviation is realized by controlling ion wind;The application effectively solves the problem of uneven distribution of trace elements in the traditional feed mixing process by actively controlling the electrostatic field distribution, improves the product quality stability, reduces the energy consumption, and has important industrial application value.

Description

Technical Field

[0001] This invention relates to the field of online feed production technology, and in particular to a method and system for online feed production management. Background Technology

[0002] In the feed production process, the addition of trace elements (such as iron, zinc, copper, manganese, etc.) is a key link to ensure the nutritional balance of feed. However, because the amount of trace elements added is small (usually at the mg / kg level), the particles are small and have different physicochemical properties, uneven distribution is likely to occur during the mixing process, which seriously affects the quality stability of feed products.

[0003] Traditional feed mixing processes rely primarily on mechanical stirring to achieve uniform distribution of components. However, during mixing, trace element particles are prone to agglomeration and segregation due to electrostatic effects. Especially in dry environments, friction between different components generates static charge accumulation, causing charged particles to migrate irregularly under electrostatic forces, thus disrupting the uniformity of the mixture. Current technologies mainly improve mixing by extending mixing time, increasing mixing intensity, or adding antistatic agents. However, these methods suffer from high energy consumption, increased costs, and the potential introduction of harmful substances. Currently, domestic and international research on feed mixing uniformity control focuses mainly on optimizing mixing equipment and adjusting process parameters. Research on the influence mechanism of electrostatic field distribution on trace element migration is relatively weak. Existing electrostatic control technologies are mostly applied in powder conveying and dust removal, and their application in feed mixing is still in its early stages.

[0004] Therefore, there is an urgent need to develop an online feed production management technology that can monitor and actively control the distribution of electrostatic fields in real time to solve the problem of uneven distribution of trace elements. Summary of the Invention

[0005] This invention provides an online feed production management method and system, aiming to solve the technical problems of uneven distribution of trace elements, unstable mixing effect, and high energy consumption in the feed mixing process in the prior art, so as to achieve precise control of the distribution of trace elements in the feed mixing process, improve product quality stability, and reduce production costs.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for online feed production management, used to improve the uniformity of electrostatic distribution of trace elements during the mixing process of online feed production, the method comprising the following steps:

[0008] Step S1: Electrostatic field strength sensors and ion generators are evenly arranged on the inner wall of the mixer. Each electrostatic field strength sensor corresponds to one ion generator. The electrostatic field strength sensors are used to monitor the distribution of electrostatic field strength at each monitoring point inside the mixer in real time during the mixing process.

[0009] Step S2: Based on the physical relationship between the electrostatic field distribution and the migration of trace elements, and considering the influence of electrostatically driven particle migration and diffusion mass transfer, a model for the distribution deviation of trace element concentration in the feed in the mixer is established.

[0010] Step S3: Based on the trace element distribution deviation model, a multi-objective optimization algorithm is used to calculate the optimal output current intensity of each ion generator. By controlling the output current intensity, an ion wind is generated to correct the distribution deviation of trace elements in the feed in the mixer.

[0011] Furthermore, the electrostatic field intensity distribution monitoring in step S1 is described using a three-dimensional spherical coordinate system, and the intensity of the electrostatic field E is expressed as:

[0012] ;in, spherical coordinate position At any moment Electric field strength, unit: kV / m; These are the electric field components in the radial, polar, and azimuth directions, respectively, with units of kV / m. Radial distance, value range: R is the radius of the mixer, in meters. The polar angle has the following range of values: , This refers to the azimuth angle, with a range of: ; Time, in seconds.

[0013] Furthermore, the spatiotemporal rate of change of the electrostatic field Defined as:

[0014] ;in, The sampling time interval is 0.1-1.0s.

[0015] Furthermore, the trace element distribution deviation model in step S2 is established based on the electrostatically driven mass transfer equation, and its mathematical expression is:

[0016] ;in, Here is the drift velocity of a charged particle in an electrostatic field: ; As the source term, consider electrostatically induced concentration changes: For the first Actual concentration distribution of trace elements, unit: mg / kg; For the first Target concentrations of trace elements, in mg / kg; For the first The effective diffusion coefficient of trace elements, in m² / s, has the following range: ; For the first The effective charge of trace element particles, in C, has a range of values: ; The mixing medium refers to the dynamic viscosity of the feed in the mixer, measured in Pa·s, with a range of values. ; For the first The equivalent radius of trace element particles, in meters, with a range of values ​​of [missing value]. .

[0017] Furthermore, the formula for calculating the deviation of trace element concentration is: ; For the first Electrostatic sensitivity coefficients of trace elements, in units of: ; For the first Concentration decay coefficient of trace elements, unit: ; This is the gradient operator.

[0018] Furthermore, based on the aforementioned trace element distribution deviation Establish an evaluation index for the uniformity of electrostatic field distribution;

[0019] Among them, the uniformity index of a single trace element for:

[0020] ;

[0021] Standard deviation of concentration deviation: Local concentration gradient index for: Where M represents the total number of trace elements added; This represents the total number of monitoring points.

[0022] The comprehensive deviation index CDI is: ;when At the same time, an influence relationship model between the ion generator and the monitoring point is established, and the optimal output current intensity of each ion generator is calculated.

[0023] Furthermore, establishing a model of the influence relationship between the ion generator and the monitoring point specifically includes: calculating the ion generator... For monitoring points Influence coefficient of electrostatic field at the location:

[0024] ; It is a geometric constant, with units of m²·V / (A·m), and a value of 10³m²·V / (A·m). The straight-line distance between ion generator j and monitoring point k is in meters. For the first The effective operating distance of each ion generator, in meters, is 2.0 meters. The angle between the main axis direction of ion generator j and the direction of the connecting line is expressed in rad.

[0025] Local electric field strength generated by the ion generator for: ; For the first The output current intensity of each ion generator, in mA; For the first Current-to-electric field conversion efficiency of an ion generator, unit: V / A, range: The superposition effect of multiple ion generators is: ; The background electrostatic field strength is expressed in kV / m.

[0026] The distance attenuation model for ion transport efficiency is as follows: ; For the first The maximum transmission efficiency of the ion generator is taken as 0.95.

[0027] Furthermore, in step S3, the calculation of the ion generator current intensity employs a multi-objective optimization algorithm, with the objective function being: .

[0028] Uniformity target for: .

[0029] Energy consumption optimization target for: ; For the first The maximum permissible output current of each ion generator.

[0030] The stability objective is: .

[0031] The constraints include: electric field balance constraints, current range constraints, safety constraints, and neighboring generator coordination constraints.

[0032] Furthermore, the multi-objective optimization algorithm is solved using an adaptive particle swarm optimization algorithm, with the particle position update equation as follows: ; For the first During the nth iteration The current intensity vector of each ion generator; For the first Particles in the next iteration Velocity vector; velocity update equation:

[0033] ;

[0034] For the first The inertia weight of the next iteration; A random number in the interval [0,1]. For particles The historical best position; The globally optimal position;

[0035] ; These are the maximum and minimum values ​​of the inertia weight, respectively.

[0036] The fitness function is: ; For the first One constraint function;

[0037] Perform 5-100 iterations until the algorithm converges: At this point, the optimal current intensity output is obtained. : .

[0038] In a second aspect, the present invention provides an online feed production management system for performing the method of the first aspect, the system comprising: an electrostatic monitoring module, a data processing module, an electrostatic control module, and a safety protection module;

[0039] The electrostatic monitoring module includes an array of electrostatic field strength sensors distributed in a spherical coordinate system and an environmental parameter monitoring unit, which is used to collect electrostatic field distribution data in the mixer in real time.

[0040] The data processing module is equipped with an embedded controller and a high-speed signal processing unit to execute micro-element distribution modeling and current intensity optimization algorithms.

[0041] The electrostatic control module includes a multi-point array of ion generators and an intelligent current control unit, which precisely controls the output current intensity of each ion generator based on the calculation results of the optimization algorithm.

[0042] The safety protection module includes an electric field strength monitoring unit and an emergency power failure protection system to ensure the safe operation of the system.

[0043] Furthermore, the electrostatic monitoring module includes: an electric field strength sensor, a sensor array layout, an environmental monitoring unit, and a data acquisition system.

[0044] The electric field strength sensor adopts an electrostatic induction sensor, with a measurement range of 0-100kV / m, an accuracy of ±0.1kV / m, a response time of ≤1ms, and an operating temperature range of -10℃ to +60℃.

[0045] The sensor array is evenly distributed on the inner wall of the mixer in spherical coordinate system, with ≥3 radial layers, ≥8 sensors per layer, and a total of ≥24 sensors.

[0046] The environmental monitoring unit monitors temperature, humidity, and air pressure in real time, with temperature accuracy of ±0.1℃, humidity accuracy of ±1%RH, and air pressure accuracy of ±0.1kPa.

[0047] The data acquisition system uses a 24-bit high-precision ADC with a sampling frequency of ≥1kHz, and data transmission uses CAN bus or Ethernet protocol.

[0048] Furthermore, the electrostatic control module includes: an ion generator array, an intelligent current control unit, a power management system, and an effect monitoring and feedback unit.

[0049] An ion generator array is configured one-to-one with an electrostatic field sensor. Each ion generator has a maximum output current of 50mA, a current adjustment accuracy of ±0.1mA, and a response time of ≤0.5s.

[0050] The intelligent current control unit is based on an adaptive particle swarm optimization algorithm, which calculates and outputs the optimal current intensity setpoint for each ion generator in real time.

[0051] The power management system has a total power of ≤5kW, with each ion generator having independent power supply and control, and is equipped with overcurrent, overvoltage, and short-circuit protection functions.

[0052] The effect monitoring and feedback unit monitors the uniformity of trace element distribution in real time. If the value is ≥0.9, the control is considered effective; otherwise, the current distribution will be automatically re-optimized.

[0053] Compared with the prior art, the beneficial effects of this invention are:

[0054] This invention overcomes the limitations of traditional passive mixing by applying active electrostatic field control technology to the feed mixing process; it accurately calculates the optimal current intensity of each ion generator through a multi-objective optimization algorithm, achieving a control precision of ±0.1 mA; and it improves the uniformity of trace element distribution. It can achieve a concentration of over 0.9, which is 15-25% higher than traditional methods; it can shorten mixing time by 20-30%; it avoids the use of traditional antistatic agents and reduces the impact of chemical additives on feed quality; it is suitable for various types of feed mixing equipment and has good versatility and important industrialization value. Attached Figure Description

[0055] Figure 1 This is a flowchart of an online feed production management method according to the present invention;

[0056] Figure 2 This is a schematic diagram of the composition of an online feed production management system according to the present invention. Detailed Implementation

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

[0058] Example 1

[0059] like Figure 1 The diagram shown is a flowchart of an online feed production management method according to the present invention, used to improve the uniformity of trace element distribution during the mixing process of online feed production. The method is characterized by comprising the following steps:

[0060] Step S1: Electrostatic field strength sensors and ion generators are evenly arranged on the inner wall of the mixer. Each electrostatic field strength sensor corresponds to one ion generator. The electrostatic field strength sensors are used to monitor the distribution of electrostatic field strength at each monitoring point inside the mixer in real time during the mixing process.

[0061] The electrostatic field intensity distribution monitoring adopts a three-dimensional spherical coordinate system. The main reasons for choosing the spherical coordinate system are: (1) it can accurately describe any position in the three-dimensional space inside the mixer; (2) it facilitates the establishment of a mathematical model of the electric field distribution; (3) it simplifies the unified processing of multi-point sensor data; the intensity of the electrostatic field E is expressed as:

[0062] ;in, spherical coordinate position At any moment Electric field strength, unit: kV / m; These are the electric field components in the radial, polar, and azimuth directions, respectively, with units of kV / m. Radial distance, value range: R is the radius of the mixer, in meters. The polar angle has the following range of values: , This refers to the azimuth angle, with a range of: ; Time, unit: seconds;

[0063] Spatiotemporal change rate of electrostatic field Defined as:

[0064] ;in, The sampling time interval is 0.1-1.0s; when the mixing process is more intense, an interval of 0.1-0.5s is used, and a 1.0s interval can be used during the stable mixing stage.

[0065] The electrostatic field strength sensors are arranged according to the principle of uniform coverage and focused monitoring. In the radial direction, the sensors are distributed in three layers at r=0.25R, r=0.5R, and r=0.75R to ensure full coverage monitoring from the center of the mixer to the sidewalls. In the angular direction, eight sensors are arranged in each layer. (k=0,1,2,...,7) are evenly distributed to form a three-dimensional array of 24 monitoring points.

[0066] Step S2: Based on the physical relationship between the electrostatic field distribution and the migration of trace elements, and considering the influence of electrostatically driven particle migration and diffusion mass transfer, a model for the distribution deviation of trace element concentration in the feed in the mixer is established.

[0067] The trace element distribution deviation model is established based on the electrostatically driven mass transfer equation, and its mathematical expression is as follows:

[0068] ;in, Here is the drift velocity of a charged particle in an electrostatic field: ; As the source term, consider electrostatically induced concentration changes: For the first Actual concentration distribution of trace elements, unit: mg / kg; For the first Target concentrations of trace elements, in mg / kg; For the first The effective diffusion coefficient of trace elements, in m² / s, has the following range: ; For the first The effective charge of trace element particles, in C, has a range of values: The effective charge of trace element particles depends on the particle material, surface roughness, and ambient humidity. Metal oxide particles (such as...) ZnO exhibits significant triboelectric effect in dry environments, with charge reaching up to [amount missing]. The charge-carrying capacity of trace elements in organic chelates is relatively weak, while the charge-carrying capacity of trace elements in organic chelates is relatively weak. The mixing medium refers to the dynamic viscosity of the feed in the mixer, measured in Pa·s, with a range of values. ; For the first The equivalent radius of trace element particles, in meters, with a range of values ​​of [missing value]. .

[0069] The formula for calculating the deviation of trace element concentration is: ; For the first Electrostatic sensitivity coefficients of trace elements, in units of: ; For the first Concentration decay coefficient of trace elements, unit: ; The gradient operator is used; the electrostatic sensitivity coefficient and concentration decay coefficient are empirical parameters describing the effect of changes in the electrostatic field on the concentration distribution, and need to be determined through experimental calibration; the electrostatic sensitivity coefficient of metallic elements is relatively large, and they respond quickly to changes in the electric field, while the decay coefficient of organic elements is relatively large, and their concentration changes relatively slowly.

[0070] Step S3: Based on the trace element distribution deviation model, a multi-objective optimization algorithm is used to calculate the optimal output current intensity of each ion generator. By controlling the output current intensity, an ion wind is generated to correct the distribution deviation of trace elements in the feed in the mixer.

[0071] Based on the aforementioned trace element distribution deviation Establish an evaluation index for the uniformity of electrostatic field distribution;

[0072] Among them, the uniformity index of a single trace element for:

[0073] ;

[0074] Standard deviation of concentration deviation: Local concentration gradient index for: Where M represents the total number of trace elements added; This represents the total number of monitoring points.

[0075] The comprehensive deviation index CDI is: ;when At the same time, an influence relationship model between the ion generator and the monitoring point is established, and the optimal output current intensity of each ion generator is calculated.

[0076] Establishing a model of the influence relationship between the ion generator and the monitoring point specifically includes: calculating the ion generator... For monitoring points Influence coefficient of electrostatic field at the location:

[0077] ; It is a geometric constant, with units of m²·V / (A·m), and a value of 10³m²·V / (A·m). The straight-line distance between ion generator j and monitoring point k is in meters. For the first The effective operating distance of each ion generator, in meters, is 2.0 meters. The angle between the main axis direction of ion generator j and the direction of the connecting line is expressed in rad.

[0078] Local electric field strength generated by the ion generator for: ; For the first The output current intensity of each ion generator, in mA; For the first Current-to-electric field conversion efficiency of an ion generator, unit: V / A, range: The superposition effect of multiple ion generators is: ; Background electrostatic field strength, unit: kV / m;

[0079] The distance attenuation model for ion transport efficiency is as follows: ;

[0080] For the first The maximum transmission efficiency of the ion generator is taken as 0.95.

[0081] The calculation of the ion generator current intensity adopts a multi-objective optimization algorithm, and the optimization objective function is: ;

[0082] Uniformity target for: ;

[0083] Energy consumption optimization target for: ; For the first The maximum permissible output current of each ion generator;

[0084] The stability objective is: ;

[0085] The constraints include: electric field balance constraints, current range constraints, safety constraints, and neighboring generator coordination constraints.

[0086] Electric field equilibrium constraints: ; The target electrostatic field strength, in kV / m;

[0087] Current range constraints: ;

[0088] Safety constraints: ; To ensure safety, the electric field strength is limited, with units of kV / m.

[0089] Proximity generator coordination constraints: This represents the maximum current difference between adjacent ion generators, in mA, with a range of 5-10 mA.

[0090] The multi-objective optimization algorithm is solved using an adaptive particle swarm optimization algorithm, with the particle position update equation as follows: ; For the first During the nth iteration The current intensity vector of each ion generator; For the first Particles in the next iteration Velocity vector; velocity update equation:

[0091] ;

[0092] For the first The inertia weight of the next iteration; A random number in the interval [0,1]. For particles The historical best position; The globally optimal position;

[0093] ; These are the maximum and minimum values ​​of the inertia weight, respectively.

[0094] The fitness function is: ; For the first One constraint function;

[0095] Perform 5-100 iterations until the algorithm converges: At this point, the optimal current intensity output is obtained. : .

[0096] When the deviation of trace element concentration at a certain monitoring point exceeds 5% of the target value, the system automatically triggers the local optimization mode, adjusting only the current output of the ion generator near that monitoring point to avoid excessive interference with the overall mixing process. In the convergence judgment of the optimization algorithm, in addition to the convergence condition of the objective function, a physical constraint check is added: requiring the electric field intensity gradient at all monitoring points to not exceed 5kV / m², ensuring the smoothness of the electrostatic field distribution, and avoiding excessive accumulation of trace elements caused by excessively strong local electric fields.

[0097] Taking a 1000L horizontal mixer in a feed mill as an example, four trace elements—Fe, Zn, Cu, and Mn—are added with target concentrations of 80 mg / kg, 100 mg / kg, 15 mg / kg, and 60 mg / kg, respectively. The mixer's inner diameter is R = 0.8 m; the number of electrostatic field sensors is N = 32 (4 layers × 8 sensors / layer); the number of ion generators is 32; the ambient temperature is 25℃, and the relative humidity is 45%; the feed's dynamic viscosity is... .

[0098] Traditional mechanical mixing method, mixing time: 180s; Fe element uniformity index: Zn element uniformity index: Cu elemental uniformity index: Mn element uniformity index: Overall uniformity index: Energy consumption: 12.5 kWh.

[0099] Using the method of this invention, the mixing time is reduced to 130s (a reduction of 27.8%); the uniformity index of each element is improved to above 0.88, and the overall uniformity index is improved by 30.4%; energy consumption is reduced to 9.8kWh (a reduction of 21.6%); the mixing time is reduced by 28%, improving production efficiency, avoiding the use of antistatic agents, and saving additive costs.

[0100] Example 2

[0101] like Figure 2 The diagram shown is a schematic representation of the composition of an online feed production management system according to the present invention. The system includes: an electrostatic monitoring module, a data processing module, an electrostatic control module, and a safety protection module.

[0102] The electrostatic monitoring module includes an array of electrostatic field strength sensors distributed in a spherical coordinate system and an environmental parameter monitoring unit, which is used to collect electrostatic field distribution data in the mixer in real time.

[0103] The electrostatic monitoring module includes: an electric field strength sensor, a sensor array layout, an environmental monitoring unit, and a data acquisition system;

[0104] The electric field strength sensor adopts an electrostatic induction sensor, with a measurement range of 0-100kV / m, an accuracy of ±0.1kV / m, a response time of ≤1ms, and an operating temperature range of -10℃ to +60℃.

[0105] The sensor array is evenly distributed on the inner wall of the mixer in spherical coordinate system, with ≥3 radial layers, ≥8 sensors per layer, and a total of ≥24 sensors;

[0106] The environmental monitoring unit monitors temperature, humidity, and air pressure in real time, with a temperature accuracy of ±0.1℃, a humidity accuracy of ±1%RH, and an air pressure accuracy of ±0.1kPa.

[0107] The data acquisition system uses a 24-bit high-precision ADC with a sampling frequency of ≥1kHz, and data transmission uses CAN bus or Ethernet protocol.

[0108] The data processing module is equipped with an embedded controller and a high-speed signal processing unit to execute micro-element distribution modeling and current intensity optimization algorithms.

[0109] The electrostatic control module includes a multi-point array of ion generators and an intelligent current control unit, which precisely controls the output current intensity of each ion generator based on the calculation results of the optimization algorithm.

[0110] The electrostatic control module includes: an ion generator array, an intelligent current control unit, a power management system, and an effect monitoring and feedback unit;

[0111] An ion generator array is configured one-to-one with an electrostatic field sensor. Each ion generator has a maximum output current of 50mA, a current adjustment accuracy of ±0.1mA, and a response time of ≤0.5s.

[0112] The intelligent current control unit is based on an adaptive particle swarm optimization algorithm, which calculates and outputs the optimal current intensity setpoint for each ion generator in real time.

[0113] The total power of the power management system is ≤5kW, and each ion generator is independently powered and controlled, with overcurrent, overvoltage, and short circuit protection functions.

[0114] The effect monitoring and feedback unit monitors the uniformity of trace element distribution in real time. If the value is ≥0.9, the control is considered effective; otherwise, the current distribution will be automatically re-optimized.

[0115] The response time of the ion generator, ≤0.5s, is primarily limited by the ion transport process. Ions migrate at a speed of approximately 1-2 m / s in air, and the transport distance from the electrode to the mixer is about 0.5-1.0 m. Therefore, the theoretical minimum response time is 0.25-0.5s. By optimizing the electrode structure and increasing the operating voltage, the actual response time can be reduced to 0.3-0.5s. The power management system employs switching power supply technology, with each ion generator equipped with an independent DC-DC converter, allowing continuous adjustment of the output voltage within the range of 5-30kV. The total power is limited to 5kW, with an average power of approximately 150W per generator, meeting the requirements for continuous operation.

[0116] The effect monitoring and feedback unit is a key function of closed-loop control. It achieves this through real-time comparison. The system automatically judges the control effect based on a set threshold of 0.9. If the value is less than 0.9 for three consecutive sampling periods, a re-optimization is triggered; if it is less than 0.9 for ten consecutive sampling periods, a re-optimization is initiated. When the value is ≥0.9, the system enters energy-saving mode, reducing the power of the ion generator to a maintenance level.

[0117] The safety protection module includes an electric field strength monitoring unit and an emergency power failure protection system to ensure the safe operation of the system.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for online feed production management, used to improve the uniformity of trace element distribution during the mixing process of online feed production, characterized in that, The method includes the following steps: Step S1: Electrostatic field strength sensors and ion generators are evenly arranged on the inner wall of the mixer. Each electrostatic field strength sensor corresponds to one ion generator. The electrostatic field strength sensors are used to monitor the distribution of electrostatic field strength at each monitoring point inside the mixer in real time during the mixing process. Step S2: Based on the physical relationship between the electrostatic field distribution and the migration of trace elements, and considering the influence of electrostatically driven particle migration and diffusion mass transfer, a model for the distribution deviation of trace element concentration in the feed in the mixer is established. Step S3: Based on the trace element distribution deviation model, a multi-objective optimization algorithm is used to calculate the optimal output current intensity of each ion generator. By controlling the output current intensity, an ion wind is generated to correct the distribution deviation of trace elements in the feed in the mixer.

2. The method according to claim 1, characterized in that, The electrostatic field intensity distribution monitoring in step S1 is described using a three-dimensional spherical coordinate system, and the intensity of the electrostatic field E is expressed as: ;in, spherical coordinate position At any moment Electric field strength, unit: kV / m; These are the electric field components in the radial, polar, and azimuth directions, respectively, with units of kV / m. Radial distance, value range: R is the radius of the mixer, in meters. The polar angle has the following range of values: , This refers to the azimuth angle, with a range of: ; Time, unit: seconds; Spatiotemporal change rate of electrostatic field Defined as: ;in, The sampling time interval is 0.1-1.0s.

3. The method according to claim 2, characterized in that, The trace element distribution deviation model in step S2 is established based on the electrostatically driven mass transfer equation, and its mathematical expression is: ;in, Here is the drift velocity of a charged particle in an electrostatic field: ; As the source term, consider electrostatically induced concentration changes: For the first Actual concentration distribution of trace elements, unit: mg / kg; For the first Target concentrations of trace elements, in mg / kg; For the first The effective diffusion coefficient of trace elements, in m² / s, has the following range: ; For the first The effective charge of trace element particles, in C, has a range of values: ; The mixing medium refers to the dynamic viscosity of the feed in the mixer, measured in Pa·s, with a range of values. ; For the first The equivalent radius of trace element particles, in meters, with a range of values ​​of [missing value]. ; The formula for calculating the deviation of trace element concentration is: ; For the first Electrostatic sensitivity coefficients of trace elements, in units of: ; For the first Concentration decay coefficient of trace elements, unit: ; This is the gradient operator.

4. The method according to claim 3, characterized in that, Based on the aforementioned trace element distribution deviation Establish evaluation indexes for the uniformity of electrostatic field distribution: Among them, the uniformity index of a single trace element for: Standard deviation of concentration deviation: Local concentration gradient index for: Where M represents the total number of trace elements added; This represents the total number of monitoring points. The comprehensive deviation index CDI is: ;when At the same time, an influence relationship model between the ion generator and the monitoring point is established, and the optimal output current intensity of each ion generator is calculated.

5. The method according to claim 4, characterized in that, Establishing a model of the influence relationship between the ion generator and the monitoring point specifically includes: calculating the ion generator... For monitoring points Influence coefficient of electrostatic field at the location: ; It is a geometric constant, with units of m²·V / (A·m), and a value of 10³m²·V / (A·m). The straight-line distance between ion generator j and monitoring point k is in meters. For the first The effective operating distance of each ion generator, in meters, is 2.0 meters. The angle between the main axis direction of ion generator j and the direction of the connecting line is expressed in rad. Local electric field strength generated by the ion generator for: ; For the first The output current intensity of each ion generator, in mA; For the first Current-to-electric field conversion efficiency of an ion generator, unit: V / A, range: ; The superposition effect of multiple ion generators is: ; Background electrostatic field strength, unit: kV / m; The distance attenuation model for ion transport efficiency is as follows: ; For the first The maximum transmission efficiency of the ion generator is taken as 0.

95.

6. The method according to claim 5, characterized in that, The calculation of the ion generator current intensity in step S3 employs a multi-objective optimization algorithm, with the objective function being: ; Uniformity target for: ; Energy consumption optimization target for: ; For the first The maximum permissible output current of each ion generator; The stability objective is: .

7. The method according to claim 6, characterized in that, The multi-objective optimization algorithm is solved using an adaptive particle swarm optimization algorithm, with the particle position update equation as follows: ; For the first During the nth iteration The current intensity vector of each ion generator; For the first Particles in the next iteration Velocity vector; velocity update equation: ； For the first The inertia weight of the next iteration; A random number in the interval [0,1]. For particles The historical best position; The globally optimal position; ; These are the maximum and minimum values ​​of the inertia weight, respectively. The fitness function is: ; For the first One constraint function; Perform 5-100 iterations until the algorithm converges: At this point, the optimal current intensity output is obtained. : .

8. A feed online production management system, used to execute the method according to any one of claims 1-7, characterized in that, The system includes: an electrostatic monitoring module, a data processing module, an electrostatic control module, and a safety protection module; The electrostatic monitoring module includes an array of electrostatic field strength sensors distributed in a spherical coordinate system and an environmental parameter monitoring unit, which is used to collect electrostatic field distribution data in the mixer in real time. The data processing module is equipped with an embedded controller and a high-speed signal processing unit, which are used to execute trace element distribution modeling and current intensity optimization algorithms. The electrostatic control module includes a multi-point array of ion generators and an intelligent current control unit, which precisely controls the output current intensity of each ion generator based on the calculation results of the optimization algorithm. The safety protection module includes an electric field strength monitoring unit and an emergency power failure protection system to ensure the safe operation of the system.

9. The system according to claim 8, characterized in that, The electrostatic monitoring module includes: an electric field strength sensor, a sensor array layout, an environmental monitoring unit, and a data acquisition system; The electric field strength sensor adopts an electrostatic induction sensor, with a measurement range of 0-100kV / m, an accuracy of ±0.1kV / m, a response time of ≤1ms, and an operating temperature range of -10℃ to +60℃. The sensor array is evenly distributed on the inner wall of the mixer in spherical coordinate system, with ≥3 radial layers, ≥8 sensors per layer, and a total of ≥24 sensors; The environmental monitoring unit monitors temperature, humidity, and air pressure in real time, with a temperature accuracy of ±0.1℃, a humidity accuracy of ±1%RH, and an air pressure accuracy of ±0.1kPa. The data acquisition system uses a 24-bit high-precision ADC with a sampling frequency of ≥1kHz, and data transmission uses CAN bus or Ethernet protocol.

10. The system according to claim 9, characterized in that, The electrostatic control module includes: an ion generator array, an intelligent current control unit, a power management system, and an effect monitoring and feedback unit. An ion generator array is configured one-to-one with an electrostatic field sensor. Each ion generator has a maximum output current of 50mA, a current adjustment accuracy of ±0.1mA, and a response time of ≤0.5s. The intelligent current control unit is based on an adaptive particle swarm optimization algorithm, which calculates and outputs the optimal current intensity setpoint for each ion generator in real time. The total power of the power management system is ≤5kW, and each ion generator is independently powered and controlled, with overcurrent, overvoltage, and short circuit protection functions. The effect monitoring and feedback unit monitors the uniformity of trace element distribution in real time. If the value is ≥0.9, the control is considered effective; otherwise, the current distribution will be automatically re-optimized.

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