Water circulation monitoring system for precision manufacturing sand

Abstract

The invention discloses a water circulation monitoring system for precision manufacturing sand, and particularly relates to the technical field of water circulation monitoring, static accumulation data is collected in real time through a static accumulation module by using a high-precision sensor to calculate a static accumulation degree coefficient, and a basis is provided for subsequent static interference analysis; the accumulation division module extracts periodic characteristics of electrostatic interference by adopting Fourier transform and wavelet analysis methods to obtain an initial electrostatic accumulation threshold value, and realizes accurate division of electrostatic accumulation states; the energy consumption difference module adaptively distributes pulsed electric fields with different intensities according to the electrostatic accumulation degree, energy consumption is optimized while electrostatic interference is suppressed, and energy efficiency performance of different control strategies is evaluated by calculating an energy consumption difference coefficient; and the iterative adjustment module optimizes an electrostatic accumulation threshold value based on a gradient descent method, and dynamically adjusts a pulse electric field control strategy to enable the system to adapt to different processing environments and water circulation states, so that the energy consumption is effectively reduced on the premise of ensuring the processing quality.

Description

Technical Field

[0001] The present invention relates to the technical field of water circulation monitoring, and more particularly to a water circulation monitoring system for precision manufacturing sand. Background Art

[0002] In the precision machining of low-temperature brittle materials (such as carbon fiber composites, ceramic-based composites, and certain polymer materials), the stability of cooling circulating water is crucial to manufacturing quality. However, due to the formation of ice crystals in water under low-temperature conditions, the mutual friction between ice crystals and sand particles will produce an electrostatic effect, resulting in electrostatic adsorption and blockage of sand particles in the filtration system, reducing water circulation efficiency and causing additional maintenance requirements. In addition, the accumulation of static electricity will also affect the measurement accuracy of water conductivity, thereby interfering with the normal operation of the water quality monitoring system. In order to solve the above problems, the prior art has integrated a pulsed electric field antistatic module into the circulating water pipeline, which can effectively reduce the electrostatic adsorption of ice crystals and sand particles and reduce interference with the conductivity sensor. However, the main disadvantage of this method is that the energy consumption increases by up to 30%, which significantly increases the operating cost of the system, especially in manufacturing scenarios with limited energy or high energy efficiency requirements (such as spacecraft component processing and polar equipment manufacturing). This problem is particularly prominent.

[0003] Therefore, how to find a balance between eliminating electrostatic interference and reducing energy consumption and optimizing the water circulation monitoring system remains a key technical challenge that needs to be urgently solved in the current precision manufacturing industry. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a water circulation monitoring system for precision manufacturing sand to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A water circulation monitoring system for precision manufacturing sand, comprising a static electricity accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module;

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

[0008] The accumulation partitioning module is used to calculate the power spectrum density of the electrostatic accumulation degree coefficient through time series analysis, and obtain the initial electrostatic accumulation threshold sequence according to the power spectrum 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 assign different pulse electric field strengths to different degrees of electrostatic interference; and calculate the energy consumption difference coefficient under different pulse electric field strength strategies;

[0010] The iterative adjustment module is used to define the static electricity accumulation threshold target optimization function, adopt the gradient descent method to iteratively optimize the static electricity accumulation threshold, and continuously update the static electricity accumulation threshold.

[0011] In a preferred embodiment, the static electricity accumulation data includes sand concentration, ice crystal volume fraction, water flow rate, and water temperature;

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

[0013] In a preferred embodiment, the time series of the electrostatic accumulation coefficient is obtained by the electrostatic accumulation module, and the power spectrum density of the electrostatic accumulation coefficient is calculated by Fourier transform, as follows: P(f) = |∫SEAC(t)*e -j2πft dt| 2 , where P(f) represents the power spectrum density of the electrostatic accumulation coefficient, which is used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient obtained at time t, and e -j2πft represents a complex exponential function, where e represents the base of a natural number, j represents an imaginary unit, π represents the ratio of circumference to circumference, f represents frequency, and t represents time. The electrostatic interference period is obtained according to the power spectrum density, as follows: in It means finding the value of f that makes the power spectrum density function P(f) reach the maximum value, and T represents the electrostatic interference period.

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

[0015] Calculate the local electrostatic perturbation factor ΔJZ, the expression is as follows Where W(T,τ k) represents the local characteristics of electrostatic interference at the kth time offset, k = {1, 2, 3, ..., K}, K is a positive integer.

[0016] In a preferred embodiment, the static electricity accumulation threshold is calculated: Among them, T1 i Indicates the starting time of the ith electrostatic interference cycle, T2 i Indicates the end time of the i-th electrostatic interference cycle; YZ i represents the electrostatic accumulation threshold of the i-th electrostatic interference cycle, i={1,2,3,...,n}, n is a positive integer;

[0017] The multiple electrostatic accumulation thresholds obtained by calculation are removed from duplicate values ​​and arranged in order from small to large to obtain the initial electrostatic accumulation threshold sequence YZ y = {YZ 1 ,YZ 2 ,...,YZ Y}, where YZ y represents the yth electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, y={1,2,3,...,Y}, and Y is a positive integer.

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

[0019] Calculate the energy consumption of the pulse electric field, the expression is as follows: En = PW*Ty, where En represents the energy consumption of the pulse electric field, PW represents the output power of the pulse electric field, and Ty represents the duration of the pulse electric field;

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

[0021] In a preferred embodiment, the static electricity accumulation threshold target optimization function is defined as follows: Among them, λ1 and λ1 are weight parameters, which are used to weigh the influence of electrostatic accumulation error and energy consumption difference;

[0022] The static electricity accumulation threshold is iteratively optimized using the gradient descent method: Where η is the learning rate, J is the electrostatic accumulation threshold target optimization function, represents the initial static electricity accumulation threshold, Indicates the updated static electricity accumulation threshold;

[0023] Gradient calculation:

[0024] Technical effects and advantages of the present invention:

[0025] 1. The present invention realizes accurate monitoring and intelligent optimization and control of electrostatic interference in cooling circulating water during precision machining of low-temperature brittle materials by integrating electrostatic accumulation module, accumulation division module, energy consumption difference module and iterative adjustment module, ensuring the stability and 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 basis 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 obtains the initial electrostatic accumulation threshold to achieve accurate division of the electrostatic accumulation state; the energy consumption difference module adaptively allocates pulse electric fields of different intensities according to the degree of electrostatic accumulation, ensuring that energy consumption is optimized while suppressing electrostatic interference, and evaluating 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 pulse electric field control strategy, so that the system can adapt to different processing environments and water circulation states, thereby effectively reducing energy consumption while ensuring processing quality. The problem of electrostatic interference affecting the water cycle monitoring accuracy and high energy consumption in the existing technology is solved. While ensuring the quality of precision manufacturing, the stability, intelligence level and energy utilization efficiency of the system are improved. It is particularly suitable for scenarios such as spacecraft component processing and polar equipment manufacturing that have extremely high requirements for water cycle stability and energy consumption management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0027] Figure 1 Flowchart of a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment: The present invention provides as Figure 1 A water circulation monitoring system for precision manufacturing sand shown includes a static electricity accumulation module, an accumulation division module, an energy consumption difference module, and an iterative adjustment module;

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

[0031] The accumulation partitioning module is used to calculate the power spectrum density of the electrostatic accumulation degree coefficient through time series analysis, and obtain the initial electrostatic accumulation threshold sequence according to the power spectrum 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 assign different pulse electric field strengths to different degrees of electrostatic interference; and calculate the energy consumption difference coefficient under different pulse electric field strength strategies;

[0033] Iterative adjustment module, used to define the static electricity accumulation threshold target optimization function, use the gradient descent method to iteratively optimize the static electricity accumulation threshold, and continuously update the static electricity accumulation threshold;

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

[0035] The static electricity accumulation data include sand concentration, ice crystal volume fraction, water flow rate, and water temperature;

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

[0037] It should be noted that the static electricity accumulation data can be obtained by high-precision sensors and setting a fixed collection cycle. The high-precision sensors include turbidity sensors, low-temperature ultrasonic attenuation sensors, electromagnetic flow meters, platinum resistance temperature sensors, etc.

[0038] In the present invention, a high-precision sensor is used to monitor the static electricity accumulation between cooling circulating water and sand particles, and a static electricity accumulation coefficient is calculated to measure the static electricity accumulation degree caused by factors such as friction between ice crystals and sand particles in the cooling circulating water system. When the static electricity accumulation coefficient is too high, it indicates that the static electricity accumulation is serious, which may affect the conductivity reading and the stability of the filtration system. Through real-time monitoring of the static electricity accumulation coefficient, the system can intelligently allocate pulse electric fields of different intensities for static electricity elimination, 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 volume fraction, water flow rate, water temperature, dielectric constant and conductivity, the physical nature of the risk of static electricity adsorption is dynamically characterized; complex static electricity phenomena are converted into numerical signals that can be monitored in real time, thereby providing a scientific basis for the precise regulation of pulse electric fields, intelligently identifying the periodic law of static electricity interference, and dynamically adjusting the intensity and action time of the pulse electric field accordingly, while suppressing static electricity adsorption blockage, avoiding all-weather high-energy consumption operation;

[0039] The accumulation partitioning module is used to calculate the power spectrum density of the electrostatic accumulation degree coefficient through time series analysis, and obtain the initial electrostatic accumulation threshold sequence according to the power spectrum 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 spectrum density of the electrostatic accumulation coefficient is calculated using Fourier transform, as follows: P(f) = |∫SEAC(t)*e -j2πft dt| 2 , where P(f) represents the power spectrum density of the electrostatic accumulation coefficient, which is used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient obtained at time t, and e -j2πft represents a complex exponential function, where e represents the base of a natural number, j represents an imaginary unit, π represents the ratio of circumference to circumference, f represents frequency, and t represents time. The electrostatic interference period is obtained according to the power spectrum density, as follows: in It means finding the value of f that makes the power spectrum density function P(f) reach the maximum value, T represents the electrostatic interference period; the local disturbance of the electrostatic interference period is analyzed by wavelet transform, as follows: Where W(T,τ) represents the local characteristics of electrostatic interference, ψ * represents the complex conjugate of the wavelet basis function, τ represents the time offset;

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

[0042] Calculate the local electrostatic perturbation factor ΔJZ, the expression is as follows Where W(T,τk ) represents the local characteristics of electrostatic interference at the kth time offset, k = {1, 2, 3, ..., K}, K is a positive integer;

[0043] Calculate the static electricity accumulation threshold: Among them, T1 i Indicates the starting time of the ith electrostatic interference cycle, T2 i Indicates the end time of the i-th electrostatic interference cycle; YZ i represents the electrostatic accumulation threshold of the i-th electrostatic interference cycle, i={1,2,3,...,n}, n is a positive integer;

[0044] The multiple electrostatic accumulation thresholds obtained by calculation are removed from duplicate values ​​and arranged in order from small to large to obtain the initial electrostatic accumulation threshold sequence YZ y = {YZ 1 ,YZ 2 ,...,YZ Y}, where YZ y represents the yth electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, y={1,2,3,...,Y}, Y is a positive integer;

[0045] The present invention calculates the power spectral density of the electrostatic accumulation coefficient through time series analysis, and obtains the initial electrostatic accumulation threshold sequence based on the power spectral density, thereby playing a vital role in the entire monitoring system. First, the power spectral density of the electrostatic accumulation coefficient is analyzed by Fourier transform to accurately extract the periodic characteristics of electrostatic interference, ensuring that the interference frequency in the system can be identified, which helps to accurately judge the global characteristics of electrostatic interference and provide a solid foundation for subsequent threshold setting. Secondly, the local disturbance of the electrostatic interference period is analyzed by wavelet transform, which can effectively capture the short-term and transient electrostatic interference characteristics. This method, which combines the global frequency analysis of Fourier transform and the local time analysis of wavelet transform, enables the system to monitor and identify electrostatic interference more comprehensively and meticulously on different time scales. By calculating the local electrostatic disturbance factor, the degree of influence of electrostatic interference is further refined, providing a more accurate adjustment basis for 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 assign different pulse electric field strengths to different degrees of electrostatic interference; and calculate the energy consumption difference coefficient under different pulse electric field strength strategies;

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

[0048] If SEAC≤YZ1 , it means that there is slight electrostatic interference, which basically does not affect the water circulation, and a low-intensity pulse electric field is used;

[0049] If YZ 1 <SEAC≤YZ 2 , it means there is moderate electrostatic interference, and a medium-intensity pulse electric field is used to prevent static electricity from accumulating too quickly;

[0050] If SEAC>YZ 2 , it means that there is strong electrostatic interference, and a high-intensity pulse electric field is used to suppress electrostatic interference;

[0051] The energy consumption difference coefficients under different pulse electric field strength strategies are calculated as follows:

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

[0053] Calculate the energy consumption difference coefficient, the expression is as follows: Where PECC represents the energy consumption difference coefficient, Eba represents the pulse electric field energy consumption when the pulse electric field maintains the maximum output power all day long, and Edy represents the weighted sum of the pulse electric field energy consumption under the actual pulse 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 the corresponding pulse electric field strength is intelligently allocated to ensure that the electrostatic interference is effectively suppressed while reducing unnecessary energy consumption. The energy consumption difference coefficient is used to measure the degree of energy consumption optimization under different pulse electric field strength strategies. Its core function is to evaluate the energy saving effect of the current pulse electric field energy consumption relative to the all-weather maximum power output, reflecting the dynamic regulation ability of the pulse electric field energy consumption and the overall energy utilization efficiency. The coefficient can quantify the difference in pulse electric field energy consumption under different electrostatic interference degrees, ensuring that the optimal energy consumption management strategy is achieved while effectively suppressing electrostatic accumulation, thereby improving the energy saving and operation stability of the system.

[0055] Iterative adjustment module, used to define the static electricity accumulation threshold target optimization function, use the gradient descent method to iteratively optimize the static electricity accumulation threshold, and continuously update the static electricity accumulation threshold;

[0056] The static electricity accumulation threshold objective optimization function is defined as follows: Among them, λ1 and λ1 are weight parameters, which are used to weigh the influence of electrostatic accumulation error and energy consumption difference;

[0057] The static electricity accumulation threshold is iteratively optimized using the gradient descent method: Where η is the learning rate, J is the electrostatic accumulation threshold target optimization function, represents the initial static electricity accumulation threshold, Indicates the updated static electricity accumulation threshold;

[0058] Gradient calculation:

[0059] The present invention defines an electrostatic accumulation threshold target optimization function and uses a gradient descent method to iteratively optimize the electrostatic accumulation threshold, so that the system can continuously update the electrostatic accumulation threshold in a dynamic environment to minimize the electrostatic accumulation error and optimize the energy consumption management. By calculating the gradient of the optimization objective function, it is ensured that the adjustment direction and step size of the electrostatic accumulation threshold are reasonable, thereby avoiding local optimal traps and accelerating convergence. During the iteration process, the threshold is dynamically optimized according to the real-time changes of electrostatic interference, so that the system can 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] The present invention realizes accurate monitoring and intelligent optimization and control of electrostatic interference in cooling circulating water during precision machining of low-temperature brittle materials by integrating electrostatic accumulation module, accumulation division module, energy consumption difference module and iterative adjustment module, ensuring the stability and 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 basis 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 obtains the initial electrostatic accumulation threshold to achieve accurate division of the electrostatic accumulation state; the energy consumption difference module adaptively allocates pulse electric fields of different intensities according to the degree of electrostatic accumulation, ensuring that energy consumption is optimized while suppressing electrostatic interference, and evaluating 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 pulse electric field control strategy, so that the system can adapt to different processing environments and water circulation states, thereby effectively reducing energy consumption while ensuring processing quality. The problem of electrostatic interference affecting the water cycle monitoring accuracy and high energy consumption in the existing technology is solved. While ensuring the quality of precision manufacturing, the stability, intelligence level and energy utilization efficiency of the system are improved. It is particularly suitable for scenarios such as spacecraft component processing and polar equipment manufacturing that have extremely high requirements for water cycle stability and energy consumption management.

[0061] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0062] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0063] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0064] If the functions are implemented in the form of 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0065] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A water circulation monitoring system for precision manufacturing sand, characterized by: It includes static electricity accumulation module, accumulation division module, energy consumption difference module and iterative adjustment module; The static electricity accumulation module is used to obtain the static electricity accumulation data of cooling circulating water and sand particles in different cycles through high-precision sensors to calculate the static electricity accumulation degree coefficient; The accumulation partitioning module is used to calculate the power spectrum density of the electrostatic accumulation degree coefficient through time series analysis, and obtain the initial electrostatic accumulation threshold sequence according to the power spectrum density of the electrostatic accumulation degree coefficient; An energy consumption difference module is used to compare the electrostatic accumulation degree coefficient with the electrostatic accumulation threshold to assign different pulse electric field strengths to different degrees of electrostatic interference; And calculate the energy consumption difference coefficient under different pulse electric field strength strategies; The iterative adjustment module is used to define the static electricity accumulation threshold target optimization function, adopt the gradient descent method to iteratively optimize the static electricity accumulation threshold, and continuously update the static electricity accumulation threshold.

2. A water circulation monitoring system for precision manufacturing sand according to claim 1, characterized in that: The static electricity accumulation data include sand concentration, ice crystal volume fraction, water flow rate, and water temperature; The static electricity accumulation coefficient SEAC is calculated based on the static electricity accumulation data. The expression is as follows Where Qs represents the sand concentration, Cice represents the ice crystal volume fraction, Vs represents the water flow rate, Ts represents the water temperature, ∈r represents the dielectric constant of water, Ec represents the electrical conductivity of water, f(Vs,Ts) represents the electrostatic cumulative impact factor of water flow rate and water temperature, 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 spectrum density of the electrostatic accumulation coefficient is calculated using Fourier transform, as follows: P(f) = |∫SEAC(t)*e -j2πft dt| 2 , where P(f) represents the power spectrum density of the electrostatic accumulation coefficient, which is used to describe the energy distribution of the electrostatic accumulation coefficient at frequency f, SEAC(t) represents the electrostatic accumulation coefficient obtained at time t, and e -j2πft represents a complex exponential function, where e represents the base of a natural number, j represents an imaginary unit, π represents the ratio of circumference to circumference, f represents frequency, and t represents time. The electrostatic interference period is obtained according to the power spectrum density, as follows: in It means finding the value of f that makes the power spectrum density function P(f) reach the maximum value, and 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 disturbance of the electrostatic interference period, as follows: Where W(T,τ) represents the local characteristics of electrostatic interference, ψ * represents the complex conjugate of the wavelet basis function, τ represents the time offset; Calculate the local electrostatic perturbation factor ΔJZ, the expression is as follows Where W(T,τ k ) represents the local characteristics of electrostatic interference at the kth time offset, k = {1, 2, 3, ..., K}, K is a positive integer.

5. A water circulation monitoring system for precision manufacturing sand according to claim 4, characterized in that: Calculate the static electricity accumulation threshold: Among them, T1 i Indicates the starting time of the ith electrostatic interference cycle, T2 i Indicates the end time of the i-th electrostatic interference cycle; YZ i represents the electrostatic accumulation threshold of the i-th electrostatic interference cycle, i={1,2,3,...,n}, n is a positive integer; The multiple electrostatic accumulation thresholds obtained by calculation are removed from duplicate values ​​and arranged in order from small to large to obtain the initial electrostatic accumulation threshold sequence YZ y ={YZ1,YZ2,...,YZ Y }, where YZ y represents the yth electrostatic accumulation threshold in the electrostatic accumulation threshold sequence, 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 coefficients under different pulse electric field strength strategies are calculated as follows: Calculate the energy consumption of the pulse electric field, the expression is as follows: En = PW*Ty, where En represents the energy consumption of the pulse electric field, PW represents the output power of the pulse electric field, and Ty represents the duration of the pulse electric field; Calculate the energy consumption difference coefficient, the expression is as follows: Where PECC represents the energy consumption difference coefficient, Eba represents the pulse electric field energy consumption when the pulse electric field maintains the maximum output power all day long, and Edy represents the weighted sum of the 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 static electricity accumulation threshold objective optimization function is defined as follows: Among them, λ1 and λ1 are weight parameters, which are used to weigh the influence of electrostatic accumulation error and energy consumption difference; The static electricity accumulation threshold is iteratively optimized using the gradient descent method: Where η is the learning rate, J is the electrostatic accumulation threshold target optimization function, represents the initial static electricity accumulation threshold, Indicates the updated static electricity accumulation threshold; Gradient calculation:

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