Crystal cleaning early warning system based on calculation

Abstract

The invention discloses a crystal cleaning early warning system based on calculation, and particularly relates to the technical field of crystal cleaning. Comprising a crystal cleaning area division module, a crystal cleaning data acquisition module, a crystal cleaning data analysis module, a crystal cleaning early warning judgment module, a crystal cleaning early warning degree analysis module, a crystal cleaning early warning judgment comprehensive analysis module and a crystal cleaning early warning man-machine interaction module, the crystal cleaning area division module is used for determining a batch of crystals as a target area and dividing the target area into a plurality of monitoring sub-areas according to each crystal, and the crystal cleaning environment parameter analysis is carried out on each monitoring sub-area in the crystal cleaning target area, so that the crystal cleaning process is ensured to be in a proper environment range; if the cleaning environment indexes exceed the standard range, early warning is given out in time, an operator is prompted to take measures, and the high-quality cleaning effect is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of crystal cleaning, and in particular to a crystal cleaning early warning system based on calculation. Background Art

[0002] A crystal is a structure composed of a large number of microscopic material units (atoms, ions, molecules, etc.) arranged in an orderly manner according to certain rules. According to different structures and properties, crystals can be divided into ionic crystals, atomic crystals, molecular crystals and metal crystals. They are widely used in electronic engineering, optics, biology, acoustics and other fields. During the use of crystals, cleaning is an important part of maintaining their performance and quality. At present, crystal cleaning is mainly achieved through manual operation and observation, as well as judgment based on experience, to achieve the purpose of crystal cleaning early warning.

[0003] Traditional crystal cleaning often relies on manual inspection and empirical judgment. Manual inspection is easily affected by human factors, resulting in inaccurate judgment or omissions; time-based cleaning cycles may not be able to adapt to real-time changes; and the monitoring of cleaning fluid parameters may also produce errors due to the lack of in-depth mining and processing of data, ultimately affecting the quality and service life of the crystal. However, as an important material, crystals will inevitably be attached with various impurities and pollutants during production, processing and use, so crystal cleaning warning is very important.

[0004] Therefore, a computing-based crystal cleaning early warning system is provided to solve the above-mentioned problems. Based on sensor technology and data analysis methods, real-time monitoring and analysis of key parameters in the cleaning process are carried out to predict and warn of possible cleaning problems, so as to achieve real-time monitoring and early warning of the crystal cleaning process, and take corresponding measures to adjust and optimize, so as to ensure the stability and consistency of the cleaning effect, thereby improving the cleaning efficiency and quality, and providing more reliable technical guarantee for the production and application of crystals. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a crystal cleaning early warning system based on calculation to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a crystal cleaning early warning system based on calculation, comprising a crystal cleaning area division module, a crystal cleaning data acquisition module, a crystal cleaning data analysis module, a crystal cleaning early warning judgment module, a crystal cleaning early warning degree analysis module, a crystal cleaning early warning judgment comprehensive analysis module, and a crystal cleaning early warning human-computer interaction module;

[0007] The crystal cleaning area division module is used to determine a batch of crystals as a target area, and divide the target area into a plurality of monitoring sub-areas according to each crystal, which are sequentially recorded as 1, 2, 3, ..., n;

[0008] The crystal cleaning data acquisition module is used to collect data from the sub-areas, obtain parameters required for various early warning indicators, and output the parameters required for various early warning indicators to the crystal cleaning data analysis module;

[0009] The crystal cleaning data analysis module includes a crystal cleaning environment index analysis unit, a crystal cleaning process index analysis unit, an equipment performance index analysis unit, and a crystal cleaning quality index analysis unit. The crystal cleaning environment index analysis unit is used to calculate the crystal cleaning environment parameters, the crystal cleaning process index analysis unit is used to calculate the crystal cleaning process parameters, the equipment performance index analysis unit is used to calculate the cleaning equipment parameters, and the crystal cleaning quality index analysis unit is used to calculate the crystal quality parameters, and output the corresponding calculations to the crystal cleaning early warning judgment module;

[0010] The crystal cleaning early warning judgment module is used to compare the various crystal cleaning parameter indicators calculated by the above-mentioned monitoring sub-areas with the threshold value, and output the judgment result to the crystal cleaning early warning degree analysis module and the crystal cleaning early warning human-computer interaction module;

[0011] The crystal cleaning early warning degree analysis module is used to analyze the early warning difference calculated above to obtain an early warning degree index, and output the analysis result to the crystal cleaning early warning judgment comprehensive analysis module;

[0012] The crystal cleaning early warning judgment comprehensive analysis module is used to comprehensively analyze the early warning degree results of each monitoring sub-area obtained by the above calculation, and output the comprehensive analysis results to the crystal cleaning early warning human-computer interaction module;

[0013] The crystal cleaning warning human-computer interaction module is used to output the warning results input from the above modules to

[0014] The crystal cleaning operator information terminal prompts the crystal cleaning operator to take corresponding measures according to the different warning levels.

[0015] Preferably, the parameters required for each early warning indicator in the crystal cleaning data acquisition module include target area information, contact information of crystal cleaning operators, crystal cleaning environment parameters, crystal cleaning process parameters, cleaning equipment parameters, and crystal quality parameters, wherein the crystal cleaning environment parameters specifically include the temperature, concentration, pH value of the cleaning liquid monitored by various sensors, and the number of times the cleaning liquid is used; the crystal cleaning process parameters specifically include the cleaning time, pressure and flow rate; the cleaning equipment parameters specifically include the operating voltage, current and temperature of the pump monitored by various sensors; the crystal quality parameters specifically include the surface roughness, optical reflectivity and impurity content monitored by various sensors.

[0016] Preferably, the model of the crystal cleaning environment index in the crystal cleaning data analysis module is: Where Cl_qi i represents the crystal cleaning environment index of the i-th monitoring sub-area, De represents the concentration of the cleaning solution, Cs_t represents the temperature of the cleaning solution, PH represents the pH value of the cleaning solution, represents the external environment temperature influence coefficient, Δt=|A_t-Cs_t|, C_t represents the cleaning time, A_t represents the external environment temperature, S_n represents the standard number of times the cleaning fluid is used, Cs_n represents the cleaning fluid and the number of times it is used, and They respectively represent the weights of cleaning solution concentration, temperature and pH value.

[0017] Preferably, the model of the crystal cleaning process index in the crystal cleaning data analysis module is: where C_qi i represents the crystal cleaning process index of the i-th monitoring sub-area, (C_t) min Indicates the minimum value of cleaning time, (C_t) max represents the maximum value of the cleaning time, Cp represents the cleaning pressure, (Cp)0 represents the standard cleaning pressure, Fl represents the cleaning flow rate, (Fl)0 represents the standard cleaning flow rate, a1, a2, b1, b2, c1, and c2 are all constants, among which (a1, a2) < 0, β1, β2 and β3 represent the weights of the cleaning time, pressure and flow rate respectively.

[0018] Preferably, the model of equipment performance index in the crystal cleaning data analysis module is: Ep i =λ1lg(UI)+λ2lg(P_t), where Ep i represents the equipment performance index of the i-th monitoring sub-area, UI represents the working voltage and current of the pump, P_t represents the working temperature of the pump, λ1 and λ2 represent the weights of the working voltage, current and temperature of the pump respectively.

[0019] Preferably, the model of the crystal cleaning quality index in the crystal cleaning data analysis module is: where Cc_qi i represents the crystal cleaning quality index of the i-th monitoring sub-area, Ra represents the value of the crystal surface roughness, m1 represents the number of multiple roughness measurements, Cr represents the value of the optical reflectivity, m2 represents the number of multiple optical reflectivity measurements, Bc represents the impurity content of the crystal before cleaning, Ac represents the impurity content of the crystal after cleaning, η1, η2 and η3 represent the weights of surface roughness, optical reflectivity and impurity removal rate, respectively.

[0020] Preferably, the early warning judgment in the crystal cleaning early warning judgment module includes the following steps:

[0021] Step 1: Obtain the crystal cleaning environment index analysis and calculation results Cl_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCl_qi i =|Cl_qi-Cl_qi i |, where ΔCl_qi i represents the warning difference, Cl_qi represents the threshold value;

[0022] Step 2: Obtain the crystal cleaning process index analysis calculation results C_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔC_qi i =|C_qi-C_qi i |, where ΔC_qi i represents the warning difference, C_qi represents the threshold value;

[0023] Step 3: Obtain the equipment performance index analysis and calculation results Ep for each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔEp i =|Ep-Ep i |, where ΔEp i represents the warning difference, Ep represents the threshold value;

[0024] Step 4: Obtain the crystal cleaning quality index analysis and calculation results Cc_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCc_qi i =|Cc_qi-Cc_qi i |, where ΔCc_qi i represents the warning difference, and Cc_qi represents the threshold.

[0025] Preferably, the warning degree analysis in the crystal cleaning warning degree analysis module includes the following steps:

[0026] Step 1: The model for obtaining the crystal cleaning environment warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0027] Step 2: The model for obtaining the crystal cleaning process warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0028] Step 3: The model for obtaining the equipment performance warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0029] Step 4: The model for obtaining the crystal cleaning quality warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0030] Preferably, the comprehensive warning judgment degree model in the crystal cleaning warning judgment comprehensive analysis module is:

[0031] Technical effects and advantages of the present invention:

[0032] 1. The present invention analyzes the crystal cleaning environmental parameters of each monitoring sub-area in the crystal cleaning target area to ensure that the crystal cleaning process is within a suitable environmental range. It is necessary to strictly control the concentration, temperature and pH value of the cleaning liquid. If the cleaning environmental indicators exceed the standard range, an early warning is issued in time to prompt the operator to take measures to ensure high-quality cleaning effects and avoid changes in cleaning environmental parameters, which may result in the inability to effectively remove impurities from the crystal surface, thereby affecting the cleaning quality.

[0033] 2. The present invention analyzes the crystal cleaning process parameters of each monitoring sub-area in the crystal cleaning target area to ensure the cleanliness of the crystal surface, the uniformity of cleaning and the integrity of the microstructure. It is necessary to strictly control the cleaning time, pressure and flow rate. If the crystal cleaning process indicators exceed the standard range, an early warning is issued in time to prompt the operator to take measures to reasonably adjust and optimize the cleaning parameters, which helps to obtain the best cleaning effect;

[0034] 3. The present invention ensures the normal operation of the equipment by analyzing the parameters of the crystal cleaning equipment in each monitoring sub-area within the crystal cleaning target area to avoid insufficient cleaning capacity of the equipment, which may lead to residual pollutants on the crystal surface. These residues may cause quality problems in subsequent processes and trigger the alarm of the early warning system. Therefore, the cleaning performance of the equipment is to ensure the cleaning quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] See also Figure 1 As shown, the present invention provides a calculation-based crystal cleaning early warning system, including a crystal cleaning area division module, a crystal cleaning data acquisition module, a crystal cleaning data analysis module, a crystal cleaning early warning judgment module, a crystal cleaning early warning degree analysis module, a crystal cleaning early warning judgment comprehensive analysis module, and a crystal cleaning early warning human-computer interaction module.

[0038] The crystal cleaning area division module is connected to the crystal cleaning data acquisition module, the crystal cleaning data analysis module is respectively connected to the crystal cleaning early warning judgment module and the crystal cleaning data acquisition module, the crystal cleaning early warning degree analysis module is respectively connected to the crystal cleaning early warning judgment comprehensive analysis module and the crystal cleaning early warning judgment module, and the crystal cleaning early warning human-computer interaction module is respectively connected to the crystal cleaning early warning judgment comprehensive analysis module and the crystal cleaning early warning judgment module.

[0039] The crystal cleaning area division module is used to determine a batch of crystals as a target area, and divide the target area into a plurality of monitoring sub-areas according to each crystal, which are sequentially recorded as 1, 2, 3, ..., n;

[0040] What needs to be specifically explained in this embodiment is that the crystal types and shapes in the target area are the same, the cleaning requirements and processes are the same, and the cleaning equipment used is the same.

[0041] The crystal cleaning data acquisition module is used to collect data from the sub-areas, obtain parameters required for various early warning indicators, and output the parameters required for various early warning indicators to the crystal cleaning data analysis module;

[0042] What needs to be specifically explained in this embodiment is that the parameters required for various early warning indicators include target area information, contact information of crystal cleaning operators, crystal cleaning environment parameters, crystal cleaning process parameters, cleaning equipment parameters, and crystal quality parameters, among which the crystal cleaning environment parameters specifically include the temperature, concentration, pH value of the cleaning liquid monitored by various sensors, and the number of times the cleaning liquid is used; the crystal cleaning process parameters specifically include the cleaning time, pressure and flow rate; the cleaning equipment parameters specifically include the operating voltage, current and temperature of the pump monitored by various sensors; the crystal quality parameters specifically include the surface roughness, optical reflectivity and impurity content monitored by various sensors.

[0043] The crystal cleaning data analysis module includes a crystal cleaning environment index analysis unit, a crystal cleaning process index analysis unit, an equipment performance index analysis unit, and a crystal cleaning quality index analysis unit. The crystal cleaning environment index analysis unit is used to calculate the crystal cleaning environment parameters, the crystal cleaning process index analysis unit is used to calculate the crystal cleaning process parameters, the equipment performance index analysis unit is used to calculate the cleaning equipment parameters, and the crystal cleaning quality index analysis unit is used to calculate the crystal quality parameters, and output the corresponding calculations to the crystal cleaning early warning judgment module. The data analysis includes the following steps:

[0044] Step 1: In order to ensure the stability and effectiveness of the cleaning process, it is necessary to monitor and adjust the crystal cleaning environmental parameters to ensure that the crystal cleaning process is always kept within the appropriate environmental parameter range. The model for obtaining crystal cleaning environmental indicators in each monitoring sub-area in the crystal cleaning data analysis module is: Where Cl_qi i represents the crystal cleaning environment index of the i-th monitoring sub-area, De represents the concentration of the cleaning solution, Cs_t represents the temperature of the cleaning solution, PH represents the pH value of the cleaning solution, represents the external environment temperature influence coefficient, Δt=|A_t-Cs_t|, C_t represents the cleaning time, A_t represents the external environment temperature, S_n represents the standard number of times the cleaning fluid is used, Cs_n represents the cleaning fluid and the number of times it is used, and Respectively represent the weights of cleaning solution concentration, temperature and pH value;

[0045] Step 2: In order to obtain the best cleaning effect, it is necessary to monitor the crystal cleaning process parameters in real time to ensure that the crystal is within the optimal cleaning process parameter range during the cleaning process. The model for obtaining crystal cleaning process indicators in each monitoring sub-area in the crystal cleaning data analysis module is: where C_qi i represents the crystal cleaning process index of the i-th monitoring sub-area, (C_t) min Indicates the minimum value of cleaning time, (C_t) max represents the maximum value of the cleaning time, Cp represents the cleaning pressure, (Cp)0 represents the standard cleaning pressure, Fl represents the cleaning flow rate, (Fl)0 represents the standard cleaning flow rate, a1, a2, b1, b2, c1, and c2 are all constants, where (a1, a2) < 0, β1, β2, and β3 represent the weights of the cleaning time, pressure, and flow rate, respectively;

[0046] Step 3: Analyze the cleaning equipment parameters to ensure the stability and continuity of the equipment operation during the cleaning process, and avoid interruption or poor cleaning effect due to insufficient power supply. The model for obtaining equipment performance indicators in each monitoring sub-area in the crystal cleaning data analysis module is: Ep i =λ1lg(UI)+λ2lg(P_t), where Ep i represents the equipment performance index of the ith monitoring sub-area, UI represents the working voltage and current of the pump, P_t represents the working temperature of the pump, λ1 and λ2 represent the weights of the working voltage, current and temperature of the pump respectively;

[0047] Step 4: By analyzing the crystal quality parameters, the degree of cleanliness of the crystal surface during the cleaning process and the effect of the cleaning process can be reflected. The model for obtaining the crystal cleaning quality index in each monitoring sub-area in the crystal cleaning data analysis module is: where Cc_qi i represents the crystal cleaning quality index of the i-th monitoring sub-area, Ra represents the value of the crystal surface roughness, m1 represents the number of multiple roughness measurements, Cr represents the value of the optical reflectivity, m2 represents the number of multiple optical reflectivity measurements, Bc represents the impurity content of the crystal before cleaning, Ac represents the impurity content of the crystal after cleaning, η1, η2 and η3 represent the weights of the surface roughness, optical reflectivity and impurity removal rate respectively;

[0048] The crystal cleaning early warning judgment module is used to compare the various crystal cleaning parameter indicators calculated by the above-mentioned monitoring sub-areas with the threshold value to determine whether the data is within the allowable range. If the real-time data exceeds the range of the standard value, an early warning is issued, and the judgment result is output to the crystal cleaning early warning degree analysis module and the crystal cleaning early warning human-computer interaction module. The early warning judgment includes the following steps:

[0049] Step 1: Obtain the crystal cleaning environment index analysis and calculation results Cl_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCl_qi i =|Cl_qi-Cl_qi i |, where ΔCl_qi i represents the warning difference, Cl_qi represents the threshold value;

[0050] Step 2: Obtain the crystal cleaning process index analysis calculation results C_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔC_qi i =|C_qi-C_qi i |, where ΔC_qi i represents the warning difference, C_qi represents the threshold value;

[0051] Step 3: Obtain the equipment performance index analysis and calculation results Ep for each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔEp i =|Ep-Ep i |, where ΔEp i represents the warning difference, Ep represents the threshold value;

[0052] Step 4: Obtain the crystal cleaning quality index analysis and calculation results Cc_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCc_qi i =|Cc_qi-Cc_qi i |, where ΔCc_qi i represents the warning difference, Cc_qi represents the threshold value;

[0053] The crystal cleaning early warning degree analysis module is used to analyze the early warning difference calculated above to obtain an early warning degree index, and output the analysis result to the crystal cleaning early warning judgment comprehensive analysis module. The early warning degree analysis includes the following steps:

[0054] Step 1: The model for obtaining the crystal cleaning environment warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0055] Step 2: The model for obtaining the crystal cleaning process warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0056] Step 3: The model for obtaining the equipment performance warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0057] Step 4: The model for obtaining the crystal cleaning quality warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

[0058] The crystal cleaning early warning judgment comprehensive analysis module is used to comprehensively analyze the early warning degree results of each monitoring sub-area obtained by the above calculation to obtain a comprehensive early warning judgment degree index, and output the comprehensive analysis results to the crystal cleaning early warning human-computer interaction module, and its model is:

[0059] The crystal cleaning early warning human-computer interaction module is used to output the early warning results transmitted by the above module to the information terminal of the crystal cleaning operator, and prompt the crystal cleaning operator to take corresponding measures according to the different warning levels, including: timely adjusting the cleaning environment parameters to ensure that the crystal is within the optimal cleaning effect range to avoid thermal damage to the crystal or affecting the cleaning effect; timely adjusting the cleaning process parameters to ensure that the crystal is within the appropriate cleaning time, pressure and flow range, so that the cleaning liquid can fully penetrate into the tiny gaps on the surface of the crystal to remove pollutants and ensure the stability of the cleaning quality; timely adjusting the equipment performance parameters to ensure the stability of various parameters during the cleaning process to avoid unstable cleaning quality due to equipment fluctuations, and ultimately ensure high-quality cleaning effects.

[0060] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0061] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A calculation-based crystal cleaning early warning system, characterized in that: It includes a crystal cleaning area division module, a crystal cleaning data acquisition module, a crystal cleaning data analysis module, a crystal cleaning early warning judgment module, a crystal cleaning early warning degree analysis module, a crystal cleaning early warning judgment comprehensive analysis module, and a crystal cleaning early warning human-computer interaction module; The crystal cleaning area division module is used to determine a batch of crystals as a target area, and divide the target area into a plurality of monitoring sub-areas according to each crystal, which are sequentially recorded as 1, 2, 3, ..., n; The crystal cleaning data acquisition module is used to collect data from the sub-areas, obtain parameters required for various early warning indicators, and output the parameters required for various early warning indicators to the crystal cleaning data analysis module; The crystal cleaning data analysis module includes a crystal cleaning environment index analysis unit, a crystal cleaning process index analysis unit, an equipment performance index analysis unit, and a crystal cleaning quality index analysis unit. The crystal cleaning environment index analysis unit is used to calculate the crystal cleaning environment parameters, the crystal cleaning process index analysis unit is used to calculate the crystal cleaning process parameters, the equipment performance index analysis unit is used to calculate the cleaning equipment parameters, and the crystal cleaning quality index analysis unit is used to calculate the crystal quality parameters, and output the corresponding calculations to the crystal cleaning early warning judgment module; The crystal cleaning early warning judgment module is used to compare the various crystal cleaning parameter indicators calculated by the above-mentioned monitoring sub-areas with the threshold value, and output the judgment result to the crystal cleaning early warning degree analysis module and the crystal cleaning early warning human-computer interaction module; The crystal cleaning early warning degree analysis module is used to analyze the early warning difference calculated above to obtain an early warning degree index, and output the analysis result to the crystal cleaning early warning judgment comprehensive analysis module; The crystal cleaning early warning judgment comprehensive analysis module is used to comprehensively analyze the early warning degree results of each monitoring sub-area obtained by the above calculation, and output the comprehensive analysis results to the crystal cleaning early warning human-computer interaction module; The crystal cleaning early warning human-computer interaction module is used to output the early warning result transmitted by the above module to the crystal cleaning operator information terminal, and prompt the crystal cleaning operator to take corresponding measures according to different early warning levels.

2. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The parameters required for each early warning indicator in the crystal cleaning data acquisition module include target area information, contact information of crystal cleaning operators, crystal cleaning environment parameters, crystal cleaning process parameters, cleaning equipment parameters, and crystal quality parameters, wherein the crystal cleaning environment parameters specifically include the temperature, concentration, pH value of the cleaning liquid monitored by various sensors, and the number of times the cleaning liquid is used; the crystal cleaning process parameters specifically include the cleaning time, pressure and flow rate; the cleaning equipment parameters specifically include the operating voltage, current and temperature of the pump monitored by various sensors; the crystal quality parameters specifically include the surface roughness, optical reflectivity and impurity content monitored by various sensors.

3. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The model of the crystal cleaning environment index in the crystal cleaning data analysis module is: Where Cl_qi i represents the crystal cleaning environment index of the i-th monitoring sub-area, De represents the concentration of the cleaning solution, Cs_t represents the temperature of the cleaning solution, PH represents the pH value of the cleaning solution, represents the external environment temperature influence coefficient, Δt=|A_t-Cs_t|, C_t represents the cleaning time, A_t represents the external environment temperature, S_n represents the standard number of times the cleaning fluid is used, Cs_n represents the cleaning fluid and the number of times it is used, and They respectively represent the weights of cleaning solution concentration, temperature and pH value.

4. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The model of the crystal cleaning process index in the crystal cleaning data analysis module is: where C_qi i represents the crystal cleaning process index of the i-th monitoring sub-area, (C_t) min Indicates the minimum value of cleaning time, (C_t) max represents the maximum value of the cleaning time, Cp represents the cleaning pressure, (Cp)0 represents the standard cleaning pressure, Fl represents the cleaning flow rate, (Fl)0 represents the standard cleaning flow rate, a1, a2, b1, b2, c1, and c2 are all constants, among which (a1, a2) < 0, β1, β2 and β3 represent the weights of the cleaning time, pressure and flow rate respectively.

5. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The model of equipment performance index in the crystal cleaning data analysis module is: Ep i =λ1lg(UI)+λ2lg(P_t), where Ep i represents the equipment performance index of the i-th monitoring sub-area, UI represents the working voltage and current of the pump, P_t represents the working temperature of the pump, λ1 and λ2 represent the weights of the working voltage, current and temperature of the pump respectively.

6. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The model of the crystal cleaning quality index in the crystal cleaning data analysis module is: where Cc_qi i represents the crystal cleaning quality index of the i-th monitoring sub-area, Ra represents the value of the crystal surface roughness, m1 represents the number of multiple roughness measurements, Cr represents the value of the optical reflectivity, m2 represents the number of multiple optical reflectivity measurements, Bc represents the impurity content of the crystal before cleaning, Ac represents the impurity content of the crystal after cleaning, η1, η2 and η3 represent the weights of surface roughness, optical reflectivity and impurity removal rate, respectively.

7. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The early warning judgment in the crystal cleaning early warning judgment module includes the following steps: Step 1: Obtain the crystal cleaning environment index analysis and calculation results Cl_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCl_qi i =|Cl_qi-Cl_qi i |, where ΔCl_qi i represents the warning difference, Cl_qi represents the threshold value; Step 2: Obtain the crystal cleaning process index analysis calculation results C_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔC_qi i =|C_qi-C_qi i |, where ΔC_qi i represents the warning difference, C_qi represents the threshold; Step 3: Obtain the equipment performance index analysis and calculation results Ep for each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔEp i =|Ep-Ep i |, where ΔEp i represents the warning difference, Ep represents the threshold value; Step 4: Obtain the crystal cleaning quality index analysis and calculation results Cc_qi from each monitoring sub-area in the crystal cleaning data analysis module i Substitute into the formula: ΔCc_qi i =|Cc_qi-Cc_qi i |, where ΔCc_qi i represents the warning difference, and Cc_qi represents the threshold.

8. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The warning degree analysis in the crystal cleaning warning degree analysis module includes the following steps: Step 1: The model for obtaining the crystal cleaning environment warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is: Step 2: The model for obtaining the crystal cleaning process warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is: Step 3: The model for obtaining the equipment performance warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is: Step 4: The model for obtaining the crystal cleaning quality warning degree index for each monitoring sub-area in the crystal cleaning warning judgment module is:

9. A calculation-based crystal cleaning early warning system according to claim 1, characterized in that: The comprehensive warning judgment degree model in the crystal cleaning warning judgment comprehensive analysis module is:

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