A water circulation monitoring system for precision manufactured sand

Abstract

The application discloses a kind of water circulation monitoring systems for precision manufacturing sand, specifically relates to water circulation monitoring technical field, and coefficient of degree of static electricity accumulation is calculated by real-time collection of static electricity accumulation data using high-precision sensor using static electricity accumulation module, provide basis for subsequent static electricity interference analysis;Accumulation division module uses fourier transform and wavelet analysis method to extract the periodicity characteristics of static electricity interference to obtain initial static electricity accumulation threshold, realize the accurate division of static electricity accumulation state;Energy consumption difference module self-adaptively allocates different intensity pulse electric field according to the degree of static electricity accumulation, ensures to suppress static electricity interference while optimizing energy consumption, and evaluates the energy efficiency performance of different control strategies by calculating energy consumption difference coefficient;Iterative adjustment module optimizes static electricity accumulation threshold based on gradient descent method, dynamically adjusts pulse electric field control strategy so that the system can adapt to different processing environments and water circulation states, thereby effectively reducing energy consumption under the premise of ensuring processing quality.

Description

Technical Field

[0001] This invention relates to the field of water cycle monitoring technology, and more specifically, to a water cycle monitoring system for sand used in precision manufacturing. Background Technology

[0002] In the precision machining of low-temperature brittle materials (such as carbon fiber composites, ceramic matrix composites, and certain polymer materials), the stability of the cooling circulating water is crucial to manufacturing quality. However, due to the formation of ice crystals in the water at low temperatures, the friction between ice crystals and sand particles generates electrostatic effects, causing sand particles to be electrostatically adsorbed and clogged in the filtration system, reducing water circulation efficiency and triggering additional maintenance requirements. Furthermore, the accumulation of static electricity can affect the accuracy of water conductivity measurements, thus interfering with the normal operation of water quality monitoring systems. To address these issues, existing technologies have integrated pulsed electric field antistatic modules into circulating water pipelines. These modules effectively reduce the electrostatic adsorption of ice crystals and sand particles and minimize interference with conductivity sensors. However, the main drawback of this method is an increase in energy consumption of up to 30%, significantly increasing the system's operating costs, especially in energy-constrained or energy-efficient manufacturing scenarios (such as spacecraft component processing and polar equipment manufacturing).

[0003] Therefore, finding a balance between eliminating electrostatic interference and reducing energy consumption, and optimizing the water circulation monitoring system, remains a key technological challenge that the precision manufacturing industry urgently needs to address. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a water circulation monitoring system for sand used in precision manufacturing, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A water circulation monitoring system for sand used in precision manufacturing includes an electrostatic accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module;

[0007] The electrostatic accumulation module is used to acquire electrostatic accumulation data of cooling circulating water and sand particles in different cycles through high-precision sensors to calculate the electrostatic accumulation degree coefficient.

[0008] The cumulative partitioning module is used to calculate the power spectral density of the electrostatic accumulation degree coefficient through time series analysis, and to obtain the initial electrostatic accumulation threshold sequence based on the power spectral density of the electrostatic accumulation degree coefficient.

[0009] The energy consumption difference module is used to compare the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold to allocate different pulse electric field intensities for different levels of electrostatic interference; and to calculate the energy consumption difference coefficient under different pulse electric field intensity strategies.

[0010] The iterative adjustment module is used to define the target optimization function of the electrostatic accumulation threshold and iteratively optimize the electrostatic accumulation threshold using the gradient descent method, continuously updating the electrostatic accumulation threshold.

[0011] In a preferred embodiment, the electrostatic accumulation data includes sand concentration, ice crystal integral, water flow rate, and water temperature;

[0012] The electrostatic accumulation coefficient SEAC is calculated based on electrostatic accumulation data, and the expression is as follows: Where Qs represents sand concentration, Cice represents ice crystal integral, Vs represents water flow velocity, Ts represents water temperature, ∈r represents the dielectric constant of water, Ec represents the conductivity of water, f(Vs,Ts) represents the electrostatic cumulative effect factor of water flow velocity and water temperature, and f(Vs,Ts)=Vs*e Ts .

[0013] In a preferred embodiment, the time series of the electrostatic accumulation degree coefficient is obtained through the electrostatic accumulation module, and the power spectral density of the electrostatic accumulation degree coefficient is calculated using Fourier transform, as follows: P(f)=|∫SEAC(t)*e -j2πft dt| 2 In the formula, P(f) represents the power spectral density of the electrostatic accumulation coefficient, used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient acquired at time t, and e -j2πft Let represent a complex exponential function, where e represents the base of the natural number, j represents the imaginary unit, π represents pi, f represents the frequency, and t represents time. The electrostatic interference period is obtained from the power spectral density, as follows: in This indicates finding the value of f that maximizes the power spectral density function P(f), where T represents the electrostatic interference period.

[0014] In a preferred embodiment, wavelet transform is used to analyze the local disturbances of the electrostatic interference period, as follows: Where W(T,τ) represents the local characteristics of electrostatic interference, ψ * τ represents the complex conjugate of the wavelet basis function, and τ represents the time offset;

[0015] The local electrostatic disturbance factor ΔJZ is calculated using the following expression: Where W(T,τ) kLet ) represent the local characteristics of electrostatic interference at the k-th time offset, where k = {1, 2, 3, ..., K}, and K is a positive integer.

[0016] In a preferred embodiment, the electrostatic accumulation threshold is calculated: T1 i T2 represents the start time of the i-th electrostatic interference cycle. i YZ represents the end time of the i-th electrostatic interference cycle; i Let represent the electrostatic accumulation threshold for the i-th electrostatic interference cycle, where i = {1, 2, 3, ..., n}, and n is a positive integer;

[0017] After removing duplicate values ​​from the calculated electrostatic accumulation thresholds, the values ​​are arranged in ascending order to obtain the initial electrostatic accumulation threshold sequence YZ. y ={YZ1,YZ2,...,YZ Y}, where YZ y Let y represent the y-th electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, where y = {1, 2, 3, ..., Y}, and Y is a positive integer.

[0018] In a preferred embodiment, the energy consumption difference coefficient under different pulsed electric field intensity strategies is calculated as follows:

[0019] The energy consumption of a pulsed electric field is calculated using the following expression: En = PW * Ty, where En represents the energy consumption of the pulsed electric field, PW represents the output power of the pulsed electric field, and Ty represents the duration of the pulsed electric field.

[0020] The energy consumption difference coefficient is calculated using the following expression: Where PECC represents the energy consumption difference coefficient, Eba represents the pulse electric field energy consumption when the pulse electric field maintains maximum output power all day long, and Edy represents the weighted sum of pulse electric field energy consumption under the actual pulse electric field intensity strategy.

[0021] In a preferred embodiment, a target optimization function for the electrostatic accumulation threshold is defined as follows: Where λ1 and λ2 are weighting parameters used to balance the impact of electrostatic accumulation error and energy consumption difference;

[0022] The electrostatic accumulation threshold is iteratively optimized using the gradient descent method. Where η is the learning rate, and J is the objective function for optimizing the electrostatic accumulation threshold. This represents the initial electrostatic accumulation threshold. This indicates the updated electrostatic accumulation threshold;

[0023] Gradient calculation:

[0024] The technical effects and advantages of this invention are as follows:

[0025] 1. This invention integrates an electrostatic accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module to achieve precise monitoring and intelligent optimization control of electrostatic interference in the cooling circulating water during the precision machining of low-temperature brittle materials, ensuring the stability and optimal energy efficiency of the water circulation system. The electrostatic accumulation module utilizes high-precision sensors to collect electrostatic accumulation data in real time and calculates the electrostatic accumulation degree coefficient, providing a foundation for subsequent electrostatic interference analysis. The accumulation division module uses Fourier transform and wavelet analysis methods to extract the periodic characteristics of electrostatic interference and obtain the initial electrostatic accumulation threshold, achieving precise division of the electrostatic accumulation state. The energy consumption difference module adaptively allocates pulsed electric fields of different intensities according to the electrostatic accumulation degree, ensuring energy consumption optimization while suppressing electrostatic interference, and evaluates the energy efficiency performance of different control strategies by calculating the energy consumption difference coefficient. The iterative adjustment module optimizes the electrostatic accumulation threshold based on the gradient descent method and dynamically adjusts the pulsed electric field control strategy, enabling the system to adapt to different machining environments and water circulation states, thereby effectively reducing energy consumption while ensuring machining quality. This technology addresses the issues of electrostatic interference affecting the accuracy of water cycle monitoring and high energy consumption in existing technologies. While ensuring the quality of precision manufacturing, it improves the stability, intelligence level, and energy utilization efficiency of the system. It is particularly suitable for scenarios with extremely high requirements for water cycle stability and energy consumption management, such as spacecraft component processing and polar equipment manufacturing. Attached Figure Description

[0026] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0027] Figure 1 This is a flowchart of the system according to an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example: The present invention provides, as follows Figure 1 The water circulation monitoring system for sand used in precision manufacturing, shown in the figure, includes an electrostatic accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module.

[0030] The electrostatic accumulation module is used to acquire electrostatic accumulation data of cooling circulating water and sand particles in different cycles through high-precision sensors to calculate the electrostatic accumulation degree coefficient.

[0031] The cumulative partitioning module is used to calculate the power spectral density of the electrostatic accumulation degree coefficient through time series analysis, and to obtain the initial electrostatic accumulation threshold sequence based on the power spectral density of the electrostatic accumulation degree coefficient.

[0032] The energy consumption difference module is used to compare the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold to allocate different pulse electric field intensities for different levels of electrostatic interference; and to calculate the energy consumption difference coefficient under different pulse electric field intensity strategies.

[0033] The iterative adjustment module is used to define the target optimization function of the electrostatic accumulation threshold and iteratively optimize the electrostatic accumulation threshold using the gradient descent method, continuously updating the electrostatic accumulation threshold.

[0034] The electrostatic accumulation module is used to acquire electrostatic accumulation data of cooling circulating water and sand particles in different cycles through high-precision sensors to calculate the electrostatic accumulation degree coefficient.

[0035] The electrostatic accumulation data includes sand concentration, ice crystal integral, water flow rate, and water temperature;

[0036] The electrostatic accumulation coefficient SEAC is calculated based on electrostatic accumulation data, and the expression is as follows: Where Qs represents sand concentration, Cice represents ice crystal integral, Vs represents water flow velocity, Ts represents water temperature, ∈r represents the dielectric constant of water, Ec represents the conductivity of water, f(Vs,Ts) represents the electrostatic cumulative effect factor of water flow velocity and water temperature, and f(Vs,Ts)=Vs*e Ts ;

[0037] It should be noted that the electrostatic accumulation data can be obtained through high-precision sensors and by setting a fixed acquisition period. The high-precision sensors include turbidity sensors, low-temperature ultrasonic attenuation sensors, electromagnetic flowmeters, platinum resistance temperature sensors, etc.

[0038] This invention uses high-precision sensors to monitor the electrostatic accumulation between cooling circulating water and sand particles, and calculates an electrostatic accumulation coefficient to measure the degree of electrostatic accumulation caused by friction and other factors in the cooling circulating water system. When the electrostatic accumulation coefficient is too high, it indicates severe electrostatic accumulation, which may affect conductivity readings and the stability of the filtration system. Through real-time monitoring of the electrostatic accumulation coefficient, the system can intelligently allocate pulse electric fields of different intensities to eliminate electrostatics, avoid unnecessary high-intensity pulse electric field operation, reduce energy waste, and improve system efficiency. By integrating multi-dimensional parameters such as sand particle concentration, ice crystal integral number, water flow rate, water temperature, dielectric constant, and conductivity, the physical nature of electrostatic adsorption risk is dynamically characterized. The complex electrostatic phenomenon is transformed into a real-time monitorable numerical signal, thereby providing a scientific basis for the precise control of the pulse electric field. The system intelligently identifies the periodicity of electrostatic interference and dynamically adjusts the intensity and duration of the pulse electric field accordingly, suppressing electrostatic adsorption and clogging while avoiding high-energy-consumption operation around the clock.

[0039] The cumulative partitioning module is used to calculate the power spectral density of the electrostatic accumulation degree coefficient through time series analysis, and to obtain the initial electrostatic accumulation threshold sequence based on the power spectral density of the electrostatic accumulation degree coefficient.

[0040] The time series of the electrostatic accumulation coefficient is obtained through the electrostatic accumulation module, and the power spectral density of the electrostatic accumulation coefficient is calculated using Fourier transform, as follows: P(f)=|∫SEAC(t)*e -j2πft dt| 2 In the formula, P(f) represents the power spectral density of the electrostatic accumulation coefficient, used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient acquired at time t, and e -j2πft Let represent a complex exponential function, where e represents the base of the natural number, j represents the imaginary unit, π represents pi, f represents the frequency, and t represents time. The electrostatic interference period is obtained from the power spectral density, as follows: in This represents finding the value of f that maximizes the power spectral density function P(f), where T represents the electrostatic interference period. Wavelet transform is used to analyze the local perturbations of the electrostatic interference period, as detailed below: Where W(T,τ) represents the local characteristics of electrostatic interference, ψ * τ represents the complex conjugate of the wavelet basis function, and τ represents the time offset;

[0041] It should be noted that in this implementation, the Daubechies wavelet basis function is selected to perform wavelet transform on the electrostatic interference period.

[0042] The local electrostatic disturbance factor ΔJZ is calculated using the following expression: Where W(T,τ)k () represents the local characteristics of electrostatic interference at the k-th time offset, where k = {1, 2, 3, ..., K}, and K is a positive integer;

[0043] Calculate the electrostatic accumulation threshold: T1 i T2 represents the start time of the i-th electrostatic interference cycle. i YZ represents the end time of the i-th electrostatic interference cycle; i Let represent the electrostatic accumulation threshold for the i-th electrostatic interference cycle, where i = {1, 2, 3, ..., n}, and n is a positive integer;

[0044] After removing duplicate values ​​from the calculated electrostatic accumulation thresholds, the values ​​are arranged in ascending order to obtain the initial electrostatic accumulation threshold sequence YZ. y ={YZ1,YZ2,...,YZ Y}, where YZ y Let y represent the y-th electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, where y = {1, 2, 3, ..., Y}, and Y is a positive integer.

[0045] This invention calculates the power spectral density of the electrostatic accumulation coefficient through time-series analysis and obtains an initial electrostatic accumulation threshold sequence based on this power spectral density, thus playing a crucial role in the entire monitoring system. First, Fourier transform analysis of the power spectral density of the electrostatic accumulation coefficient accurately extracts the periodic characteristics of electrostatic interference, ensuring the identification of interference frequencies in the system. This helps accurately determine the global characteristics of electrostatic interference and provides a solid foundation for subsequent threshold setting. Second, wavelet transform analysis of the local disturbances in the electrostatic interference period effectively captures short-term and transient electrostatic interference characteristics. This method, combining global frequency analysis with local time analysis using Fourier transform and local time analysis using wavelet transform, enables the system to perform more comprehensive and detailed monitoring and identification of electrostatic interference at different time scales. By calculating the local electrostatic disturbance factor, the impact of electrostatic interference is further refined, providing a more accurate basis for adjusting the electrostatic accumulation threshold under different interference scenarios.

[0046] The energy consumption difference module is used to compare the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold to allocate different pulse electric field intensities for different levels of electrostatic interference; and to calculate the energy consumption difference coefficient under different pulse electric field intensity strategies.

[0047] In an optional example, the electrostatic accumulation level coefficient is compared with the electrostatic accumulation threshold to assign different pulse electric field intensities to different levels of electrostatic interference, as follows:

[0048] If SEAC ≤ YZ1, it indicates a slight electrostatic interference, which basically does not affect the water cycle, and a low-intensity pulsed electric field is adopted;

[0049] If YZ1 < SEAC ≤ YZ2, it indicates a medium electrostatic interference. To prevent the rapid accumulation of static electricity, a medium-intensity pulsed electric field is adopted;

[0050] If SEAC > YZ2, it indicates a strong electrostatic interference, and a high-intensity pulsed electric field is adopted to suppress the electrostatic interference;

[0051] Calculate the energy consumption difference coefficient under different pulsed electric field intensity strategies, as follows:

[0052] Calculate the pulsed electric field energy consumption. The expression is as follows: En = PW * Ty, where En represents the pulsed electric field energy consumption, PW represents the output power of the pulsed electric field, and Ty represents the duration of the pulsed electric field;

[0053] Calculate the energy consumption difference coefficient. The expression is as follows: Among them, PECC represents the energy consumption difference coefficient, Eba represents the pulsed electric field energy consumption when the pulsed electric field maintains the maximum output power all day long, and Edy represents the weighted sum of the pulsed electric field energy consumption under the actual pulsed electric field intensity strategy;

[0054] In the present invention, by comparing the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold, different degrees of electrostatic interference are accurately identified and corresponding pulsed electric field intensities are intelligently allocated to ensure effective suppression of electrostatic interference while reducing unnecessary energy consumption. The energy consumption difference coefficient is used to measure the energy consumption optimization degree under different pulsed electric field intensity strategies. Its core role is to evaluate the energy-saving effect of the current pulsed electric field energy consumption relative to the all-day maximum power output, reflecting the dynamic regulation ability of the pulsed electric field energy consumption and the overall energy utilization efficiency. This coefficient can quantify the pulsed electric field energy consumption difference under different electrostatic interference degrees, ensure optimal energy consumption management strategies while effectively suppressing electrostatic accumulation, and improve the energy-saving performance and operation stability of the system;

[0055] The iterative adjustment module is used to define the electrostatic accumulation threshold target optimization function and perform iterative optimization of the electrostatic accumulation threshold using the gradient descent method, continuously updating the electrostatic accumulation threshold;

[0056] Define the electrostatic accumulation threshold target optimization function, as follows: Among them, λ1 and λ1 are weight parameters used to balance the influence of electrostatic accumulation error and energy consumption difference;

[0057] Perform iterative optimization of the electrostatic accumulation threshold using the gradient descent method: Among them, η is the learning rate, and J is the electrostatic accumulation threshold target optimization function. This represents the initial electrostatic accumulation threshold. This indicates the updated electrostatic accumulation threshold;

[0058] Gradient calculation:

[0059] This invention defines an objective optimization function for the electrostatic accumulation threshold and uses gradient descent to iteratively optimize the threshold. This allows the system to continuously update the electrostatic accumulation threshold under dynamic conditions, minimizing electrostatic accumulation error and optimizing energy management. By calculating the gradient of the objective function, the adjustment direction and step size of the electrostatic accumulation threshold are ensured to be reasonable, thereby avoiding local optima traps and accelerating convergence. During the iteration process, the threshold is dynamically optimized according to the real-time changes in electrostatic interference, enabling the system to adapt to different interference levels, optimize the pulse electric field control strategy, improve the electrostatic suppression effect of the water circulation system, and reduce unnecessary energy consumption, thereby improving the overall stability, energy efficiency, and economy of the system.

[0060] This invention integrates an electrostatic accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module to achieve precise monitoring and intelligent optimization control of electrostatic interference in the cooling circulating water during the precision machining of low-temperature brittle materials, ensuring the stability and optimal energy efficiency of the water circulation system. The electrostatic accumulation module uses high-precision sensors to collect electrostatic accumulation data in real time and calculates the electrostatic accumulation degree coefficient, providing a foundation for subsequent electrostatic interference analysis. The accumulation division module uses Fourier transform and wavelet analysis methods to extract the periodic characteristics of electrostatic interference and obtain the initial electrostatic accumulation threshold, achieving precise division of the electrostatic accumulation state. The energy consumption difference module adaptively allocates pulsed electric fields of different intensities according to the electrostatic accumulation degree, ensuring energy consumption optimization while suppressing electrostatic interference, and evaluates the energy efficiency performance of different control strategies by calculating the energy consumption difference coefficient. The iterative adjustment module optimizes the electrostatic accumulation threshold based on the gradient descent method and dynamically adjusts the pulsed electric field control strategy, enabling the system to adapt to different machining environments and water circulation states, thereby effectively reducing energy consumption while ensuring machining quality. This technology addresses the issues of electrostatic interference affecting the accuracy of water cycle monitoring and high energy consumption in existing technologies. While ensuring the quality of precision manufacturing, it improves the stability, intelligence level, and energy utilization efficiency of the system. It is particularly suitable for scenarios with extremely high requirements for water cycle stability and energy consumption management, such as spacecraft component processing and polar equipment manufacturing.

[0061] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0062] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0063] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A water circulation monitoring system for sand used in precision manufacturing, characterized in that: It includes a static electricity accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module; The electrostatic accumulation module is used to acquire electrostatic accumulation data of cooling circulating water and sand particles in different cycles through high-precision sensors and calculate the electrostatic accumulation degree coefficient. The cumulative partitioning module is used to calculate the power spectral density of the electrostatic accumulation degree coefficient through time series analysis, and to obtain the initial electrostatic accumulation threshold sequence based on the power spectral density of the electrostatic accumulation degree coefficient. The energy consumption difference module is used to compare the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold to allocate different pulse electric field intensities for different levels of electrostatic interference; And calculate the energy consumption difference coefficient under different pulse electric field intensity strategies; The iterative adjustment module is used to define the target optimization function of the electrostatic accumulation threshold and iteratively optimize the electrostatic accumulation threshold using the gradient descent method, continuously updating the electrostatic accumulation threshold.

2. The water circulation monitoring system for precision manufacturing sand according to claim 1, characterized in that: The electrostatic accumulation data includes sand concentration, ice crystal integral, water flow rate, and water temperature; The electrostatic accumulation coefficient SEAC is calculated based on electrostatic accumulation data, and the expression is as follows: Where Qs represents sand concentration, Cice represents ice crystal integral, Vs represents water flow velocity, Ts represents water temperature, ∈r represents the dielectric constant of water, Ec represents the conductivity of water, f(Vs,Ts) represents the electrostatic cumulative effect factor of water flow velocity and water temperature, and f(Vs,Ts)=Vs*e Ts .

3. A water circulation monitoring system for precision manufacturing sand according to claim 2, characterized in that: The time series of the electrostatic accumulation coefficient is obtained through the electrostatic accumulation module, and the power spectral density of the electrostatic accumulation coefficient is calculated using Fourier transform, as follows: P(f)=|∫SEAC(t)*e -j2πft dt| 2 In the formula, P(f) represents the power spectral density of the electrostatic accumulation coefficient, used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient acquired at time t, and e -j2πft Let represent a complex exponential function, where e represents the base of the natural number, j represents the imaginary unit, π represents pi, f represents the frequency, and t represents time. The electrostatic interference period is obtained from the power spectral density, as follows: in This indicates finding the value of f that maximizes the power spectral density function P(f), where T represents the electrostatic interference period.

4. A water circulation monitoring system for precision manufacturing sand according to claim 3, characterized in that: Wavelet transform is used to analyze the local disturbances of the electrostatic interference period, as follows: Where W(T,τ) represents the local characteristics of electrostatic interference, ψ * τ represents the complex conjugate of the wavelet basis function, and τ represents the time offset; The local electrostatic disturbance factor ΔJZ is calculated using the following expression: Where W(T,τ) k Let ) represent the local characteristics of electrostatic interference at the k-th time offset, where k = {1, 2, 3, ..., K}, and K is a positive integer.

5. A water circulation monitoring system for precision manufacturing sand according to claim 4, characterized in that: Calculate the electrostatic accumulation threshold: T1 i T2 represents the start time of the i-th electrostatic interference cycle. i This indicates the end time of the i-th electrostatic interference cycle; YZ i Let represent the electrostatic accumulation threshold for the i-th electrostatic interference cycle, where i = {1, 2, 3, ..., n}, and n is a positive integer; After removing duplicate values ​​from the calculated electrostatic accumulation thresholds, the values ​​are arranged in ascending order to obtain the initial electrostatic accumulation threshold sequence YZ. y ={YZ1,YZ2,...,YZ Y }, where YZ y Let y represent the y-th electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, where y = {1, 2, 3, ..., Y}, and Y is a positive integer.

6. A water circulation monitoring system for precision manufacturing sand according to claim 5, characterized in that: The energy consumption difference coefficient under different pulsed electric field intensity strategies is calculated as follows: The energy consumption of a pulsed electric field is calculated as follows: En = PW * Ty, where En represents the energy consumption of the pulsed electric field, PW represents the output power of the pulsed electric field, and Ty represents the duration of the pulsed electric field. The energy consumption difference coefficient is calculated using the following expression: Where PECC represents the energy consumption difference coefficient, Eba represents the pulse electric field energy consumption when the pulse electric field maintains maximum output power all day long, and Edy represents the weighted sum of pulse electric field energy consumption under the actual pulse electric field intensity strategy.

7. A water circulation monitoring system for precision manufacturing sand according to claim 6, characterized in that: The objective optimization function for the electrostatic accumulation threshold is defined as follows: Where λ1 and λ2 are weighting parameters used to balance the impact of electrostatic accumulation error and energy consumption difference; The electrostatic accumulation threshold is iteratively optimized using the gradient descent method. Where η is the learning rate, and J is the objective function for optimizing the electrostatic accumulation threshold. This represents the initial electrostatic accumulation threshold. This indicates the updated electrostatic accumulation threshold; Gradient calculation:

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