A production data analysis management system and method based on cloud computing

Abstract

The present invention discloses a production data analysis management system and method based on cloud computing, which relates to the technical field of graphene production management systems. After the cloud computing module obtains material data and operation data from the cloud storage module, it comprehensively calculates the material data and operation data to generate a correction coefficient. The threshold comparison module compares the correction coefficient with the preset threshold, and analyzes the current processing state of the graphene according to the comparison result. When the current processing state of the graphene needs to reduce the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic device through the correction coefficient. After the graphene is reduced, the management system monitors the material data of the graphene and monitors the equipment data in real time to determine whether the ultrasonic intensity needs to be adjusted, so that the graphene can be dispersed with ultrasonic waves of appropriate intensity, avoiding damage to the graphene structure while ensuring the stable operation of the ultrasonic device.

Description

Technical Field

[0001] The present invention relates to the technical field of graphene production management system, and specifically to a production data analysis management system and method based on cloud computing. Background Art

[0002] Graphene is a single-layer honeycomb structure composed of carbon atoms with excellent electrical conductivity, thermal conductivity and mechanical strength. It is considered a revolutionary material with the potential to be applied in multiple fields such as electronics, energy, medical and materials science. Therefore, graphene production has huge economic and scientific research value. The production of graphene is a complex process that requires precise control and monitoring. The production process includes multiple stages such as raw material preparation, chemical reaction, and mechanical processing. Each stage requires detailed data recording and analysis to ensure product quality and production efficiency.

[0003] The prior art has the following deficiencies:

[0004] The production of graphene requires raw material pretreatment, oxidation, reduction, dispersion and stabilization. Usually, the reduced graphene is dispersed by ultrasonic equipment. In order to ensure the processing efficiency and quality of graphene, fixed-intensity ultrasonic waves are used to disperse the graphene, and manual adjustment is used to change the ultrasonic intensity. However, when structural changes occur during the graphene processing (such as interlayer forces and the size of the flakes are different from expectations) or abnormalities occur in the ultrasonic equipment, it is impossible for humans to detect and adjust the ultrasonic intensity in time. First, the graphene structure may be damaged, resulting in reduced quality of the finished product, and the ultrasonic equipment may also fail.

[0005] Therefore, the present invention proposes a cloud computing-based production data analysis and management system that can monitor and adjust ultrasonic intensity in a timely manner to ensure the quality of graphene finished products while avoiding ultrasonic equipment failures. Summary of the invention

[0006] The purpose of the present invention is to provide a production data analysis management system and method based on cloud computing to address the deficiencies in the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a cloud computing-based production data analysis and management system, comprising a material monitoring module, an equipment monitoring module, a processing module, a cloud storage module, a cloud computing module, a threshold comparison module, a correction module and a warning module;

[0008] Material monitoring module: used to monitor the material data of graphene raw materials after the graphene raw materials undergo the reduction process;

[0009] Equipment monitoring module: when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment, it is used to monitor the operating data of the ultrasonic equipment in real time;

[0010] Processing module: used to pre-process material data and operation data, and then upload the material data and operation data to the cloud storage module for storage;

[0011] Cloud computing module: after obtaining material data and operation data from the cloud storage module, the correction coefficient is generated by comprehensively calculating the material data and operation data;

[0012] Threshold comparison module: compares the correction coefficient with the preset threshold, and analyzes the current processing status of graphene based on the comparison results;

[0013] Correction module: when the current processing state of graphene needs to be analyzed and the ultrasonic intensity needs to be reduced, the ultrasonic intensity of the ultrasonic device is adjusted through the correction coefficient;

[0014] Warning module: When the current processing status of the graphene analysis cannot be continued, the ultrasonic equipment is controlled to shut down and a warning signal is sent to the administrator.

[0015] Preferably, the cloud computing module obtains material data and operation data from the cloud storage module, the material data includes graphene dispersion and Raman spectrum similarity, and the operation data includes ultrasonic amplitude anomaly index.

[0016] Preferably, the cloud computing module comprehensively calculates the graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index to obtain the correction coefficient gz x , the expression is:

[0017]

[0018] Where fs m is the graphene dispersion, lx m is the Raman spectrum similarity, cs b is the ultrasonic amplitude anomaly index, α, γ, and β are the proportional coefficients of graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index, respectively, and α, γ, and β are all greater than 0.

[0019] Preferably, the threshold comparison module compares the correction coefficient with a preset threshold, and analyzes the current processing state of the graphene according to the comparison result;

[0020] The preset threshold includes a first threshold and a second threshold, and the first threshold < the second threshold, and the correction coefficient gz is obtained x After the value is calculated, the correction factor gz x The value is compared with a first threshold and a second threshold;

[0021] If the correction factor gz x Value > second threshold, indicating that the current processing state of graphene cannot continue;

[0022] If the correction factor gz x The value ≤ the first threshold value indicates that the current processing state of graphene is good and can be continued;

[0023] If the first threshold < correction coefficient gz x A value ≤ the second threshold value indicates that the current processing state of graphene is good and can be continued, but the ultrasonic intensity needs to be reduced.

[0024] Preferably, when analyzing the current processing state of graphene and requiring to reduce the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic device by the correction coefficient, comprising the following steps:

[0025] When the first threshold < correction coefficient gz x When the value is ≤ the second threshold, the correction coefficient gz x The value is used to adjust the initial ultrasonic intensity of the ultrasonic device to obtain the corrected ultrasonic intensity, and the expression is:

[0026]

[0027] In the formula, gz b To correct the ultrasonic intensity, cs b is the initial ultrasonic intensity, gz x To correct the coefficient, after obtaining the corrected ultrasonic intensity, the correction module controls the ultrasonic device to perform dispersion treatment on the graphene with the corrected ultrasonic intensity.

[0028] Preferably, if the correction factor gz x The value is greater than the second threshold value, indicating that the current processing state of the graphene cannot continue, and the warning module controls the ultrasonic equipment to shut down and sends a warning signal to the administrator.

[0029] Preferably, the calculation expression of the graphene dispersion is:

[0030]

[0031] Where fs m is the graphene dispersion, D90 represents 90% of the particle size in the particle size distribution, D10 represents 10% of the particle size in the particle size distribution, and D50 represents 50% of the particle size in the particle size distribution.

[0032] Preferably, the calculation expression of the Raman spectrum similarity is:

[0033]

[0034] In the formula, lx m represents the Raman spectrum similarity, W·G is the inner product of the real-time Raman spectrum vector and the standard Raman spectrum vector, ‖W‖ and ‖G‖ are the modulus length of the real-time Raman spectrum vector and the modulus length of the standard Raman spectrum vector, respectively.

[0035] Preferably, the calculation expression of the ultrasonic amplitude abnormality index is:

[0036]

[0037] In the formula, cs b Represents the ultrasonic amplitude anomaly index, N represents the number of sampling points, and x represents the amplitude value of each sampling point.

[0038] The present invention also provides a production data analysis management method based on cloud computing, the management method comprising the following steps:

[0039] S1: The monitoring end monitors the material data of the graphene raw material after the graphene raw material undergoes the reduction process, and monitors the operation data of the ultrasonic equipment in real time when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment;

[0040] S2: After the processing end pre-processes the material data and operation data, it uploads the material data and operation data to the cloud platform for storage;

[0041] S3: The cloud platform comprehensively calculates the material data and operation data to generate a correction coefficient, compares the correction coefficient with a preset threshold, and analyzes the current processing status of graphene based on the comparison results;

[0042] S4: when the ultrasonic intensity needs to be reduced due to the analysis of the current processing state of the graphene, the ultrasonic intensity of the ultrasonic device is adjusted by the correction coefficient;

[0043] S5: When the current processing status of the graphene analysis cannot be continued, the ultrasonic device is controlled to stop and a warning signal is sent to the administrator.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] 1. The present invention uses a material monitoring module to monitor the material data of the graphene raw material after the graphene raw material undergoes a reduction process. The equipment monitoring module monitors the operation data of the ultrasonic equipment in real time when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment. The cloud computing module obtains the material data and the operation data from the cloud storage module, and then comprehensively calculates the material data and the operation data to generate a correction coefficient. The threshold comparison module compares the correction coefficient with the preset threshold, and analyzes the current processing state of the graphene based on the comparison result. When the current processing state of the graphene needs to reduce the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic equipment through the correction coefficient. After the graphene is reduced, the management system monitors the material data of the graphene, and monitors the equipment data in real time to determine whether the ultrasonic intensity needs to be adjusted, so that the graphene can be dispersed with ultrasonic waves of appropriate intensity, avoiding damage to the graphene structure while ensuring the stable operation of the ultrasonic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0049] Example 1: Please refer to Figure 1 As shown, the cloud computing-based production data analysis and management system described in this embodiment includes a material monitoring module, an equipment monitoring module, a processing module, a cloud storage module, a cloud computing module, a threshold comparison module, a correction module and a warning module;

[0050] Material monitoring module: used to monitor the material data of the graphene raw material after the graphene raw material undergoes the reduction process, and the material data is sent to the processing module;

[0051] Data collection: After the reduction process is completed, relevant data of the graphene raw materials need to be collected; these data may include information such as the quality, morphology, structure, purity, etc. of the material;

[0052] Data collection can be done by various instruments and sensors, such as transmission electron microscopy (TEM), Raman spectrometer, X-ray diffraction (XRD), etc.

[0053] Data storage: The collected data needs to be stored in the system's database for subsequent analysis and recording; cloud computing can provide a secure and scalable data storage solution;

[0054] Data analysis: Analyze the collected data to assess the quality of the graphene; the analysis may include the following aspects:

[0055] Structural analysis: using tools such as transmission electron microscopy to observe and measure features such as the number of graphene layers and lattice structure;

[0056] Composition analysis: Use Raman spectroscopy, XRD and other techniques to determine the chemical composition and purity of graphene;

[0057] Morphological analysis: evaluate the morphology, size distribution and dispersion state of graphene;

[0058] Physical properties: measuring the electrical, thermal and mechanical properties of graphene;

[0059] Quality control: Based on the analysis results, quality control decisions are made; if the graphene is found not to meet the quality standards, corresponding measures can be taken, such as adjusting production parameters, reprocessing or scrapping;

[0060] Report generation: Generate reports of monitoring and analysis results, including graphene quality assessment, characteristic data, and recommended improvement measures; reports can be used to record quality data and share with relevant personnel to support decision making;

[0061] It should be noted that when the analytical graphene has been processed through the first few steps of dispersion and the processing quality is already unqualified, the dispersion step is not performed.

[0062] Equipment monitoring module: when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment, it is used to monitor the operating data of the ultrasonic equipment in real time, and the operating data is sent to the processing module;

[0063] Sensor data acquisition: Install sensors in ultrasonic equipment to monitor the operating status and performance of the equipment in real time; these sensors may include temperature sensors, pressure sensors, ultrasonic transmitting and receiving sensors, etc.

[0064] Sensor data collection is the basis for real-time monitoring of equipment performance;

[0065] Real-time data transmission: The collected sensor data needs to be transmitted to the monitoring system in real time for analysis and recording; cloud computing technology can be used to achieve real-time data transmission;

[0066] Data Analysis: Real-time analysis of sensor data to assess the operating status of the ultrasonic equipment; analysis may include the following:

[0067] Temperature analysis: monitor the temperature of the equipment to ensure it is within the safe operating range;

[0068] Pressure analysis: monitors the pressure of ultrasonic equipment to ensure that processing parameters meet requirements;

[0069] Ultrasonic output analysis: Check the signal quality and stability of ultrasonic transmission and reception;

[0070] Intensity analysis: evaluate whether the intensity of the ultrasonic wave meets the set requirements;

[0071] Abnormal detection and alarm: If the monitoring system detects abnormal operation of the equipment or parameters beyond the safe range, an alarm should be issued immediately; the alarm can notify the relevant operators through email, SMS or system interface;

[0072] When the ultrasonic wave is no longer usable, the graphene dispersion process is stopped.

[0073] Processing module: used to pre-process material data and operation data, and then upload the material data and operation data to the cloud storage module for storage;

[0074] Data cleaning and preprocessing: First, obtain raw data from data sources (including material monitoring modules and equipment monitoring modules); perform data cleaning, including removing noise, outliers, or incomplete data points; this helps ensure the accuracy and reliability of the data; perform data preprocessing, including data smoothing, interpolation, missing value filling and other operations to make the data suitable for further analysis and storage;

[0075] Data format conversion: According to the requirements of the cloud storage module, the pre-processed data is converted into an appropriate format, usually a standard data format or protocol, for uploading and storage; Data upload to cloud storage: Upload the processed material data and operation data to the cloud storage module; Cloud storage can be a cloud server, database or other cloud platform services; When uploading data, ensure the security and integrity of the data, and encryption and permission control may be required to protect the data;

[0076] Data recording and metadata addition: When uploading data, the metadata information of the data is recorded, including the data source, collection time, data processing steps, data owner, etc. Metadata facilitates data traceability and management;

[0077] Data checksum and verification: After uploading the data, data checksum and verification are performed to ensure that the data is not damaged or lost during the upload process; to ensure the consistency and accuracy of the data for subsequent analysis and application;

[0078] Data backup and archiving: Establish data backup and archiving strategies to ensure long-term preservation and accessibility of data; this helps meet compliance and regulatory requirements;

[0079] Notifications and Reports: After data upload is complete, a notification or report can be generated to inform relevant stakeholders that the data has been successfully uploaded; this can be an automated notification or manually triggered by an operator;

[0080] Data management and maintenance: Establish data management strategies, including data retention period, data access control, and data version management; ensure data security and maintainability.

[0081] Cloud computing module: After obtaining material data and operation data from the cloud storage module, the material data and operation data are comprehensively calculated to generate correction coefficients, and the correction coefficients are sent to the threshold comparison module and the correction module.

[0082] Threshold comparison module: compares the correction coefficient with the preset threshold, analyzes the current processing status of graphene based on the comparison result, and sends the processing status to the warning module and the correction module.

[0083] Correction module: When the ultrasonic intensity needs to be reduced due to the analysis of the current processing state of graphene, the ultrasonic intensity of the ultrasonic device is adjusted through the correction coefficient.

[0084] Warning module: When the current processing status of the graphene analysis cannot be continued, the ultrasonic equipment is controlled to shut down and a warning signal is sent to the administrator.

[0085] This application uses a material monitoring module to monitor the material data of the graphene raw material after the graphene raw material has undergone a reduction process. The equipment monitoring module monitors the operating data of the ultrasonic equipment in real time when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment. After the cloud computing module obtains the material data and operating data from the cloud storage module, it comprehensively calculates the material data and operating data to generate a correction coefficient. The threshold comparison module compares the correction coefficient with the preset threshold, and analyzes the current processing state of the graphene based on the comparison result. When the analysis of the current processing state of the graphene requires reducing the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic equipment through the correction coefficient. After the graphene is reduced, the management system monitors the material data of the graphene, and monitors the equipment data in real time to determine whether the ultrasonic intensity needs to be adjusted, so that the graphene can be dispersed with ultrasonic waves of appropriate intensity, avoiding damage to the graphene structure while ensuring the stable operation of the ultrasonic equipment.

[0086] Embodiment 2: After the cloud computing module obtains the material data and the operation data from the cloud storage module, the cloud computing module comprehensively calculates the material data and the operation data to generate a correction coefficient;

[0087] The cloud computing module obtains material data and operation data from the cloud storage module, the material data includes graphene dispersion and Raman spectrum similarity, and the operation data includes ultrasonic amplitude anomaly index;

[0088] The calculation expression of graphene dispersion is:

[0089]

[0090] Where fs m is the graphene dispersion, D90 represents the 90% particle size in the particle size distribution, D10 represents the 10% particle size in the particle size distribution, and D50 represents the 50% particle size in the particle size distribution;

[0091] The smaller the dispersion value, the more uniform the particle size distribution and the better the dispersion effect. Therefore, if the dispersion is large, it means that there is particle aggregation and measures need to be taken to improve the dispersion effect, such as reducing the ultrasonic intensity.

[0092] The calculation expression of Raman spectrum similarity is:

[0093]

[0094] In the formula, lx m represents the Raman spectrum similarity, W·G is the inner product of the real-time Raman spectrum vector and the standard Raman spectrum vector, ‖W‖ and ‖G‖ are the modulus length of the real-time Raman spectrum vector and the modulus length of the standard Raman spectrum vector respectively;

[0095] The calculation expression of the real-time Raman spectrum vector modulus is:

[0096]

[0097] The calculation expression of the standard Raman spectrum vector modulus is:

[0098]

[0099] Where W represents the real-time Raman spectrum vector, b is the number of elements in the real-time Raman spectrum vector W, which usually corresponds to the number of spectral data points. a is the ath element in the real-time Raman spectrum vector, i.e., a data point of the real-time spectrum data. G represents the standard Raman spectrum vector. d is the number of elements in the standard Raman spectrum vector G, which usually corresponds to the number of spectrum data points. G c It is the cth element in the standard Raman spectrum, that is, a data point of the standard spectrum data.

[0100] The specific calculation steps are as follows:

[0101] 1) The real-time Raman spectrum and the standard Raman spectrum are represented as numerical vectors. Usually, each spectral data point can be regarded as an element in the vector;

[0102] 2) Calculate the dot product between the real-time Raman spectrum vector W and the standard Raman spectrum vector G;

[0103] 3) Calculate the norm (modulus) of the real-time Raman spectrum vector W and the standard Raman spectrum vector G;

[0104] 4) The cosine similarity is calculated using the cosine similarity formula. The value of cosine similarity is between -1 and 1. The closer the value is to 1, the more similar the two spectra are. The closer the value is to -1, the less similar the two spectra are. That is, the greater the Raman spectrum similarity value, the better the processing quality of graphene. The smaller the Raman spectrum similarity value, the worse the processing quality of graphene, and the more it is necessary to reduce the ultrasonic intensity.

[0105] The steps to obtain the real-time Raman spectrum vector are as follows:

[0106] 1) Data acquisition: Use a Raman spectrometer or other equipment to collect real-time Raman spectral data; these data are usually expressed in terms of wave number or wavelength as the horizontal axis and intensity or signal value as the vertical axis;

[0107] 2) Data preprocessing: Preprocess the real-time Raman spectroscopy data to eliminate interference factors such as noise and baseline drift; this can include processing steps such as spectral smoothing, baseline correction and peak fitting;

[0108] 3) Numericalization: converting the preprocessed real-time Raman spectroscopy data into a numerical vector; usually, each data point corresponds to an element in the vector;

[0109] 4) Normalization: In order to perform cosine similarity calculation, it is usually necessary to normalize the real-time Raman spectrum vector to have unit length (norm is 1).

[0110] The steps to obtain the standard Raman spectrum vector are as follows:

[0111] 1) Known standard spectrum: For standard Raman spectra, they are usually measured and recorded under laboratory conditions for known samples or reference materials; the acquisition of standard spectra usually includes prior knowledge and the preparation of standard experimental conditions;

[0112] 2) Data preprocessing: Similar to real-time spectroscopy, standard Raman spectroscopy also requires data preprocessing to eliminate noise and interference and ensure data quality;

[0113] 3) Numericalization: convert the preprocessed standard Raman spectrum data into a numerical vector, where each data point corresponds to an element in the vector;

[0114] 4) Normalization: As with real-time spectroscopy, the standard Raman spectral vector usually needs to be normalized to ensure that it has unit length.

[0115] The calculation expression of ultrasonic amplitude abnormal index is:

[0116]

[0117] In the formula, cs b It represents the ultrasonic amplitude anomaly index, N represents the number of sampling points, and x represents the amplitude value of each sampling point. The ultrasonic amplitude anomaly index is obtained by summing the squares of the amplitude values ​​within a period of time and then taking the square root. The larger the ultrasonic amplitude anomaly index is, the more abnormal the ultrasonic amplitude of the ultrasonic equipment is.

[0118] The cloud computing module calculates the graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index to obtain the correction coefficient gz x , the expression is:

[0119]

[0120] Where fs m is the graphene dispersion, lx m is the Raman spectrum similarity, cs b is the ultrasonic amplitude anomaly index, α, γ, and β are the proportional coefficients of graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index, respectively, and α, γ, and β are all greater than 0.

[0121] This application uses a cloud computing module to comprehensively calculate the graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index to obtain a correction coefficient. It not only comprehensively considers graphene data and equipment data to make the analysis more comprehensive, but also effectively improves data processing efficiency.

[0122] The threshold comparison module compares the correction coefficient with the preset threshold and analyzes the current processing status of the graphene based on the comparison result;

[0123] In this application, the correction factor gz x The larger the value, the worse the current processing state of graphene. The preset thresholds include a first threshold and a second threshold, and the first threshold is less than the second threshold. Therefore, the correction coefficient gz is obtained. x After the value is calculated, the correction factor gz x The value is compared with a first threshold and a second threshold;

[0124] If the correction factor gz xValue > second threshold, indicating that the current processing state of graphene cannot continue;

[0125] If the correction factor gz x The value ≤ the first threshold value indicates that the current processing state of graphene is good and can be continued;

[0126] If the first threshold < correction coefficient gz x A value ≤ the second threshold value indicates that the current processing state of graphene is good and can be continued, but the ultrasonic intensity needs to be reduced.

[0127] When the current processing state of the graphene needs to be analyzed to reduce the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic device through the correction coefficient, including the following steps:

[0128] When the first threshold < correction coefficient gz x When the value is ≤ the second threshold, the correction coefficient gz x The value is used to adjust the initial ultrasonic intensity of the ultrasonic device to obtain the corrected ultrasonic intensity, and the expression is:

[0129]

[0130] In the formula, gz b To correct the ultrasonic intensity, cs b is the initial ultrasonic intensity, gz x To correct the coefficient, after obtaining the corrected ultrasonic intensity, the correction module controls the ultrasonic device to perform dispersion treatment on the graphene with the corrected ultrasonic intensity.

[0131] When the current processing state of the graphene analysis cannot be continued, the warning module controls the ultrasonic equipment to stop and sends a warning signal to the administrator;

[0132] If the correction factor gz x If the value is greater than the second threshold, it indicates that the current processing state of the graphene cannot continue. The warning module controls the ultrasonic device to shut down and sends a warning signal to the administrator. After receiving the warning signal, the administrator needs to manage the ultrasonic device in time.

[0133] Embodiment 3: This embodiment describes a cloud computing-based production data analysis and management method, the management method comprising the following steps:

[0134] The monitoring end monitors the material data of the graphene raw material after the graphene raw material has undergone the reduction process. When the graphene raw material enters the ultrasonic equipment for ultrasonic treatment, the monitoring end monitors the operating data of the ultrasonic equipment in real time. After the processing end pre-processes the material data and the operating data, the material data and the operating data are uploaded to the cloud platform for storage. The cloud platform comprehensively calculates the material data and the operating data to generate a correction coefficient, compares the correction coefficient with the preset threshold, and analyzes the current processing state of the graphene based on the comparison result. When the analysis of the current processing state of the graphene needs to reduce the ultrasonic intensity, the ultrasonic intensity of the ultrasonic equipment is adjusted by the correction coefficient. When the analysis of the current processing state of the graphene cannot continue, the ultrasonic equipment is controlled to shut down and a warning signal is sent to the administrator.

[0135] 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.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0137] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cloud computing-based production data analysis and management system, characterized by: It includes material monitoring module, equipment monitoring module, processing module, cloud storage module, cloud computing module, threshold comparison module, correction module and warning module; Material monitoring module: used to monitor the material data of graphene raw materials after the graphene raw materials undergo the reduction process; Equipment monitoring module: when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment, it is used to monitor the operating data of the ultrasonic equipment in real time; Processing module: used to pre-process material data and operation data, and then upload the material data and operation data to the cloud storage module for storage; Cloud computing module: after obtaining material data and operation data from the cloud storage module, the correction coefficient is generated by comprehensively calculating the material data and operation data; Threshold comparison module: compares the correction coefficient with the preset threshold, and analyzes the current processing status of graphene based on the comparison results; Correction module: when the current processing state of graphene needs to be analyzed and the ultrasonic intensity needs to be reduced, the ultrasonic intensity of the ultrasonic device is adjusted through the correction coefficient; Warning module: When the current processing status of graphene analysis cannot be continued, the ultrasonic equipment is controlled to stop and a warning signal is sent to the administrator; The cloud computing module acquires material data and operation data from the cloud storage module, the material data including graphene dispersion and Raman spectrum similarity, and the operation data including ultrasonic amplitude anomaly index; The cloud computing module comprehensively calculates the graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index to obtain the correction coefficient. , the expression is: In the formula, is the graphene dispersion, is the Raman spectrum similarity, is the ultrasonic amplitude abnormality index, , , are the proportional coefficients of graphene dispersion, Raman spectrum similarity, and ultrasonic amplitude anomaly index, respectively, and , , All are greater than 0; The threshold comparison module compares the correction coefficient with the preset threshold, and analyzes the current processing state of the graphene according to the comparison result; The preset threshold includes a first threshold and a second threshold, and the first threshold < the second threshold, and the correction coefficient is obtained After the value is The value is compared with a first threshold and a second threshold; If the correction coefficient Value > second threshold, indicating that the current processing state of graphene cannot continue; If the correction coefficient The value ≤ the first threshold value indicates that the current processing state of graphene is good and can be continued; If the first threshold < correction coefficient The value is ≤ the second threshold, indicating that the current processing state of graphene is good and can be continued, but the ultrasonic intensity needs to be reduced; When the current processing state of the graphene needs to be analyzed to reduce the ultrasonic intensity, the correction module adjusts the ultrasonic intensity of the ultrasonic device through the correction coefficient, including the following steps: When the first threshold < correction coefficient When the value is less than or equal to the second threshold, the correction coefficient The value is used to adjust the initial ultrasonic intensity of the ultrasonic device to obtain the corrected ultrasonic intensity, and the expression is: In the formula, To correct the ultrasonic intensity, is the initial ultrasonic intensity, After obtaining the corrected ultrasonic intensity for the correction coefficient, the correction module controls the ultrasonic device to perform dispersion treatment on the graphene with the corrected ultrasonic intensity; If the correction coefficient The value is greater than the second threshold value, indicating that the current processing state of the graphene cannot continue, and the warning module controls the ultrasonic device to shut down and sends a warning signal to the administrator; The calculation expression of the graphene dispersion is: ； In the formula, is the graphene dispersion, Indicates the particle size at which 90% of the particle size distribution is located. Indicates the particle size at 10% of the particle size distribution, Indicates the particle size at which 50% of the particle size distribution is found; The calculation expression of the Raman spectrum similarity is: ； In the formula, represents the Raman spectrum similarity, is the inner product of the real-time Raman spectrum vector and the standard Raman spectrum vector, They are respectively the vector modulus length of the real-time Raman spectrum and the vector modulus length of the standard Raman spectrum; The calculation expression of the ultrasonic amplitude abnormality index is: ； In the formula, Represents the ultrasonic amplitude anomaly index, N represents the number of sampling points, and x represents the amplitude value of each sampling point.

2. A cloud computing-based production data analysis and management method, implemented based on the management system of claim 1, characterized in that: The management method comprises the following steps: S1: The monitoring end monitors the material data of the graphene raw material after the graphene raw material undergoes the reduction process, and monitors the operation data of the ultrasonic equipment in real time when the graphene raw material enters the ultrasonic equipment for ultrasonic treatment; S2: After the processing end pre-processes the material data and operation data, it uploads the material data and operation data to the cloud platform for storage; S3: The cloud platform comprehensively calculates the material data and operation data to generate a correction coefficient, compares the correction coefficient with a preset threshold, and analyzes the current processing status of graphene based on the comparison results; S4: when the ultrasonic intensity needs to be reduced due to the analysis of the current processing state of the graphene, the ultrasonic intensity of the ultrasonic device is adjusted by the correction coefficient; S5: When the current processing status of the graphene analysis cannot be continued, the ultrasonic device is controlled to stop and a warning signal is sent to the administrator.

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