Dispersion prediction method and electronic device

Abstract

The application provides a dispersion degree prediction method and an electronic device. During stirring of slurry by a stirring motor, the electronic device can acquire an electrical parameter of the stirring motor, so as to predict the dispersion degree of the slurry according to the electrical parameter of the stirring motor, and realize automatic determination of the dispersion degree of the slurry. Moreover, the process of determining the dispersion degree of the slurry is simplified without involving stopping of the stirring motor, sampling, testing and other processes of the slurry, so that the determination efficiency of the dispersion degree of the slurry can be improved.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically to a dispersion prediction method and electronic device. Background Technology

[0002] In the production of lithium batteries, the mixing of slurry is frequently involved to ensure that the slurry dispersion meets the requirements. To ensure efficiency, mixing equipment such as mixers is generally used to mix the slurry.

[0003] In related technologies, the mixing equipment is typically stopped after the mixing process is completed. Then, relevant testing personnel take samples of the slurry and use testing equipment such as laser particle size analyzers and scanning electron microscopes to test the samples and determine the slurry dispersion.

[0004] However, determining the dispersion of slurry involves steps such as stopping the mixing equipment, manual sampling, and testing, making the process cumbersome and resulting in low efficiency. Summary of the Invention

[0005] The embodiments of the present invention provide a dispersion prediction method and electronic device, which can improve the technical problem of low efficiency in determining the dispersion of slurry.

[0006] In a first aspect, embodiments of the present invention provide a dispersion prediction method applied to electronic devices, the method comprising:

[0007] During the mixing of the slurry by the mixing motor, the electrical parameters of the mixing motor are acquired;

[0008] The predicted dispersion of the slurry is obtained by predicting the electrical parameters of the stirring motor.

[0009] In this application, during the mixing of the slurry by the agitator motor, the electrical parameters of the agitator motor can reflect the current characteristics of the slurry. Therefore, the electronic equipment can use the electrical parameters of the agitator motor to make predictions to determine the predicted dispersion of the slurry, thereby achieving automatic determination of the slurry dispersion. Furthermore, it eliminates the need to stop the agitator motor, as well as the need for slurry sampling and testing, simplifying the process of determining the slurry dispersion and thus improving the efficiency of determining the slurry dispersion.

[0010] In one embodiment, the process of predicting the dispersion of the slurry based on the electrical parameters of the stirring motor may include:

[0011] First, feature extraction is performed on the electrical parameters to obtain feature data. Then, the feature data is input into the dispersion scoring model to obtain the predicted dispersion of the slurry.

[0012] Based on this, electronic devices can input characteristic data of electrical parameters into the dispersion scoring model, so as to evaluate the dispersion of the slurry using the dispersion scoring model and achieve rapid determination of dispersion.

[0013] In one embodiment, the aforementioned feature data includes time-domain feature data and frequency-domain feature data. Accordingly, the electronic device can perform time-domain feature extraction on the electrical parameters to obtain time-domain feature data. And, the electronic device can perform frequency-domain feature extraction on the electrical parameters to obtain frequency-domain feature data.

[0014] The above dispersion scoring model represents the relationship between the dispersion, time-domain feature data, and frequency-domain feature data, so that the dispersion scoring model can predict the dispersion of the slurry based on the current operating status of the stirring motor.

[0015] In one embodiment, after obtaining the predicted dispersion corresponding to the slurry, the method further includes:

[0016] In the event of an abnormal predicted dispersion, an anomaly handling operation is performed; wherein the anomaly handling operation includes adjusting the operating parameters of the stirring motor and / or adjusting the amount of solvent added corresponding to the slurry.

[0017] Therefore, when the dispersion is abnormal, the electronic equipment can perform abnormal handling operations to adjust the operating parameters of the stirring motor and the amount of solvent added to the slurry, so that the dispersion of the slurry meets the requirements. This allows for timely adjustment of the slurry dispersion and avoids the slurry from becoming unusable. Furthermore, by adjusting the operating parameters of the stirring motor, it can be made to operate at suitable parameters instead of always operating at fixed parameters, ensuring operational flexibility and thus avoiding resource waste.

[0018] In one embodiment, the above-mentioned anomaly handling operation includes adjusting the operating parameters of the stirring motor and adjusting the amount of solvent added corresponding to the slurry. Accordingly, the process of performing the anomaly handling operation in the event of the predicted dispersion anomaly may include:

[0019] If the predicted dispersion is lower than a first threshold, the characteristic data of the slurry are determined based on the characteristic data of the stirring motor; wherein the characteristic data is determined based on the electrical parameters of the stirring motor.

[0020] Determine the target operating parameters of the stirring motor and the target amount of solvent to be added, corresponding to the characteristic data of the slurry;

[0021] Control the stirring motor according to the target operating parameters;

[0022] The solvent is added to the slurry according to the target addition amount.

[0023] Therefore, when the slurry dispersion is low, the characteristic data of the stirring motor can be used to determine the current characteristics of the slurry. Then, the operation of the stirring motor is controlled based on the target operating parameters of the stirring motor that match the current characteristics, and solvent is added to the slurry according to the target addition amount of the stirring motor that matches the current characteristics to improve the dispersion of the slurry.

[0024] In one embodiment, the process of determining the characteristic data of the slurry may include:

[0025] Based on the feature data and combined with the first model, the characteristic data of the slurry are determined; wherein, the first model represents the correlation between the feature data of the stirring motor and the characteristic data of the slurry.

[0026] Based on this, the characteristic data of the slurry is calculated by using a first model that reflects the correlation between the characteristic data of the stirring motor and the characteristic data of the slurry, and the current characteristic data of the stirring motor. This enables the automatic and rapid determination of the characteristic data of the slurry without the need for sampling or determination by related testing equipment.

[0027] In one embodiment, the aforementioned characteristic data may include the average current and / or power of the stirring motor; the characteristic data of the slurry may include the viscosity of the slurry. Accordingly, the first model represents the correlation between the average current of the stirring motor and the viscosity of the slurry;

[0028] Alternatively, the first model represents the relationship between the power of the stirring motor and the viscosity of the slurry.

[0029] Therefore, considering that the current of the stirring motor can reflect the viscosity of the slurry, the viscosity of the slurry can be determined by the current of the stirring motor. Alternatively, considering that the power of the stirring motor can reflect the viscosity of the slurry, the viscosity of the slurry can be determined by the power of the stirring motor.

[0030] In one embodiment, the electronic device can also provide early warning. During the operation of the stirring motor, if the stirring motor meets preset process abnormality conditions, it indicates that a process abnormality may occur. Therefore, the operating parameters of the stirring motor can be adjusted in a timely manner to achieve early warning of process abnormalities and prevent serious process abnormalities from occurring.

[0031] During the operation of the stirring motor, if the stirring motor meets the preset mechanical fault conditions, it indicates that the stirring motor may have a mechanical fault. Therefore, the stirring motor can be controlled to stop running, realizing a mechanical fault early warning and avoiding serious mechanical faults in the stirring motor.

[0032] Optionally, the aforementioned preset process anomaly condition includes the power fluctuation variance of the stirring motor being greater than or equal to a preset variance threshold; wherein the preset variance threshold is determined based on historical power fluctuation variances. Therefore, when the power fluctuation variance of the stirring motor is greater than or equal to the preset variance threshold, it indicates that the stirring motor is operating abnormally, and the likelihood of a process anomaly is high; thus, a process anomaly warning can be issued.

[0033] Optionally, the preset mechanical fault conditions include one or more of the following: the high-frequency noise energy of the stirring motor is greater than or equal to a preset time; the remaining life of the bearing of the stirring motor is less than a preset life; or the peak current ratio of the stirring motor is greater than a preset current ratio. Based on this, when the high-frequency noise energy of the stirring motor is relatively long, the remaining life of the bearing of the stirring motor is relatively short, and the peak current ratio of the stirring motor is relatively large, it indicates that the stirring motor may fail, thus enabling a mechanical fault early warning system.

[0034] In a second aspect, embodiments of the present invention provide an electronic device, comprising:

[0035] A memory on which computer programs are stored;

[0036] A processor for executing the computer program in the memory to implement the method as described in the first aspect above.

[0037] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0038] Fourthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect above. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the dispersion prediction method provided in an embodiment of the present invention. Figure 1 ;

[0041] Figure 2 This is a schematic diagram of the electrical parameter acquisition process provided in an embodiment of the present invention;

[0042] Figure 3 This is a flowchart illustrating the dispersion prediction method provided in an embodiment of the present invention. Figure 2 ;

[0043] Figure 4-5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0044] Figure 6 This is a schematic diagram of the chip system provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, it should be understood that the specific embodiments described herein are only for illustration and explanation of the present invention and are not intended to limit the present invention. In the present invention, unless otherwise stated, directional terms such as "upper" and "lower" generally refer to the upper and lower positions of the device in actual use or operation, specifically the drawing directions in the accompanying drawings; while "inner" and "outer" refer to the outline of the device.

[0046] In related technologies, the process of determining the dispersion of slurry generally includes stopping the mixing equipment after the mixing process is completed. Then, relevant testing personnel take samples of the slurry and use testing equipment such as laser particle size analyzers and scanning electron microscopes to test the samples and determine the dispersion of the slurry.

[0047] However, determining the dispersion of a slurry involves steps such as stopping the mixing equipment, manual sampling, and testing, making the process cumbersome and inefficient. Furthermore, the high cost of testing equipment contributes to the overall expense. Additionally, the involvement of testing personnel in sampling and testing processes, which can affect the final dispersion determination, necessitates a high level of expertise from the testing personnel.

[0048] Therefore, to address the aforementioned problems, this application proposes a method for predicting slurry dispersion. Considering that the dispersion of the slurry affects the operation of the mixing motor in the mixing equipment, that is, it affects the electrical parameters of the mixing motor. Conversely, the electrical parameters of the mixing motor can also reflect the dispersion of the slurry. Therefore, electronic equipment can directly use the electrical parameters of the mixing motor to predict the dispersion of the slurry, achieving automatic determination of the dispersion, simplifying the dispersion determination process, and thus improving the efficiency of dispersion determination. Furthermore, it eliminates the need for manual sampling and testing, thus eliminating the need for specialized testing personnel. Additionally, since dispersion is predicted using electrical parameters, testing equipment is also unnecessary, effectively reducing costs. Moreover, it eliminates the need to wait until the mixing process is completed to determine the slurry dispersion, ensuring the timeliness of the dispersion determination.

[0049] The implementation process of the above dispersion prediction method will be described in detail below. For example, as shown... Figure 1 As shown, the method may include S101-S102.

[0050] S101. During the mixing of slurry by the mixing motor, obtain the electrical parameters of the mixing motor.

[0051] In the embodiments of this application, such as Figure 2 As shown, the electronic device is electrically connected to the data acquisition module. This data acquisition module is used to collect the electrical parameters of the motor (i.e., the mixing motor) of the mixing equipment. During the mixing of the slurry, the electronic device can obtain the electrical parameters of the mixing motor in real time or periodically through the data acquisition module.

[0052] Optionally, such as Figure 2 As shown, the electrical parameters include the current and / or voltage of the stirring motor. Accordingly, the data acquisition module may include a current sensing device and / or a voltage sensing device. The current sensing device is used to collect the current of the stirring motor. The voltage sensing device is used to collect the voltage of the stirring motor. The data acquisition module is electrically connected to the stirring motor. Taking a data acquisition module including both a current sensing device and a voltage sensing device as an example, the current sensing device can be connected in series with the three-phase power supply lines of the stirring motor, and the voltage sensor can be connected in parallel to the output terminal of the frequency converter.

[0053] Furthermore, the aforementioned current sensing device can be a Hall effect current sensor. The aforementioned voltage sensing device can be an isolated voltage transmitter.

[0054] In some embodiments, the electronic device can search from the process parameter library (or operating parameter library) whether the process parameters (or operating parameters) correspond to the formulation of the above slurry.

[0055] If there are operating parameters corresponding to the above slurry formulation, the electronic equipment can control the operation of the stirring motor according to the found operating parameters, that is, adjust the operating parameters of the stirring motor to the found operating parameters so that the stirring motor can operate according to the appropriate operating parameters, reduce the number of dynamic adjustments during the stirring process, and reduce the total stirring time.

[0056] If there are no operating parameters corresponding to the above slurry formulation, the electronic equipment can operate according to the initial operating parameters (or general operating parameters).

[0057] S102. Based on the electrical parameters of the stirring motor, the predicted dispersion of the slurry is obtained.

[0058] In this embodiment, when the slurry dispersion is low, the stirring resistance is high, and the stirring motor needs to output more torque to maintain the rotation speed, resulting in a significant increase in the power of the stirring motor, which in turn leads to a significant increase in the electrical parameters such as the current of the stirring motor. Conversely, when the slurry dispersion is high, the stirring resistance is low, and the stirring motor does not need to output more torque to maintain the rotation speed, resulting in lower power of the stirring motor, which in turn leads to lower electrical parameters such as the current of the stirring motor. It is evident that there is a correlation between the slurry dispersion and the electrical parameters of the stirring motor. Since the electrical parameters of the stirring motor are easy to obtain, the electronic device can use the electrical parameters of the stirring motor to predict the slurry dispersion, that is, obtain the predicted dispersion of the slurry, thereby enabling the determination of the slurry dispersion during stirring and achieving timely determination of the dispersion.

[0059] In some embodiments, after obtaining the electrical parameters of the motor, the electronic device can perform signal preprocessing and digital conversion on the electrical parameters to improve signal quality, thereby enabling the prediction of slurry dispersion using the processed and converted electrical parameters. Optionally, the electronic device can employ a low-pass filter to filter the motor's electrical parameters to eliminate high-frequency noise, thus achieving signal preprocessing.

[0060] Optionally, the cutoff frequency of the low-pass filter can be 100Hz or 500Hz.

[0061] Optionally, the electronic device can use a data acquisition card to digitize the electrical parameters. The sampling rate of the data acquisition card can be 1 kHz.

[0062] In some embodiments, the electronic device can extract features from electrical parameters to obtain feature data. Then, the electronic device can input the feature data into a dispersion scoring model to obtain the predicted dispersion of the slurry.

[0063] Based on this, electronic devices can input characteristic data of electrical parameters into the dispersion scoring model, allowing the model to assess the dispersion of the slurry and achieve rapid determination of the dispersion. Furthermore, compared to electrical parameters, the characteristic data of electrical parameters better reflect the operating status of the stirring motor, thus predicting the dispersion of the slurry more accurately.

[0064] Understandably, electronic devices can also directly input electrical parameters into the dispersion scoring model, which then predicts the dispersion of the slurry based on these electrical parameters. Optionally, the dispersion scoring model can first calculate the characteristic data of the electrical parameters, and then use this characteristic data to calculate the dispersion of the slurry.

[0065] In some embodiments, the aforementioned feature data may include time-domain feature data and frequency-domain feature data. Accordingly, the electronic device may perform time-domain feature extraction on the electrical parameters to obtain time-domain feature data. And, the electronic device may perform frequency-domain feature extraction on the electrical parameters to obtain frequency-domain feature data.

[0066] Optionally, the aforementioned time-domain characteristic data may include the mean current (I0). avg ), power fluctuation variance (P) var Peak current ratio (I) peak / I avg One or more of the following.

[0067] The average current reflects the overall load level of the mixing motor. When the slurry is poorly dispersed, the current of the mixing motor increases, which means the average current increases, or in other words, the load increases. For example, increased viscosity leads to an increase in load.

[0068] Power fluctuation variance can reflect the stability of the slurry flow field. An increase in power fluctuation variance indicates local agglomeration or uneven shearing of the slurry.

[0069] Optionally, the aforementioned frequency domain characteristics may include the dominant frequency amplitude (A). f0 ) and / or the proportion of harmonic energy (∑Af) 1-5 / A f0 Furthermore, the main frequency amplitude can be 0 to 50 Hz.

[0070] Optionally, the aforementioned main frequency amplitude can be obtained through a fast fourier transform (FFT).

[0071] Optionally, the above dispersion scoring model represents the relationship between dispersion, time-domain feature data, and frequency-domain feature data, so that the dispersion scoring model can predict the dispersion of the slurry based on the current operating status of the stirring motor.

[0072] Furthermore, the aforementioned dispersion scoring model actually performs a weighted combination of time-domain feature data and frequency-domain feature data to quantify dispersion. For example, the dispersion scoring model can be an example of a learning model, such as a random forest model or a support vector machine (SVM) model.

[0073] Specifically, the dispersion scoring model can be The values ​​of a1, a2, a3, and a4 can be set according to requirements. For example, a1 is 0.89, a2 is 0.32, a3 is 0.18, and a4 is 0.11. Correspondingly, the electronic device can transmit I... avg P var A f0 ,∑A f1-5 / A f0 As input parameters to the dispersion scoring model, the dispersion scoring model can output a dispersion value that can be between 0 and 100%.

[0074] In some embodiments, the dispersion scoring model described above can be trained using multiple sets of sample data. After training, the trained dispersion scoring model can be cross-validated for accuracy. If the accuracy is less than a preset accuracy, the electronic device can continue training the dispersion scoring model. If the accuracy is greater than or equal to the preset accuracy, the dispersion scoring model training is complete, and the electronic device can use the dispersion scoring model to predict dispersion.

[0075] Optionally, the preset accuracy can be 90%. Additionally, the fitting accuracy R of the dispersion scoring model... 2 =0.89.

[0076] In some embodiments, the sample data described above may include sample feature data and the corresponding sample dispersion of the sample slurry. The sample feature data can be referred to the description of feature data above. The sample dispersion corresponding to the sample feature data may be the actual dispersion of the sample slurry when the stirring motor is the same as the sample feature data.

[0077] It is understandable that the dispersion of a slurry can be reflected by its characteristic data (such as viscosity). Therefore, the sample data mentioned above can include sample characteristic data and its corresponding characteristic data of the sample slurry, as well as the sample dispersion.

[0078] The characteristic data of the slurry can be determined by relevant sensors or models. For example, if the characteristic data is viscosity, the viscosity of the sample slurry can be determined by a viscosity sensor (such as a rheometer) or the first model described below (the determination process can be found below, but will not be described here). In addition, the relevant sensor can also be used to verify whether the dispersion predicted by the dispersion scoring model is accurate.

[0079] In some embodiments, the sample slurry can be of one or more types, such as positive electrode, negative electrode, diaphragm coating, and other related slurry types. This allows the dispersion scoring model to predict the dispersion of different types of slurries, improving the model's generalization ability. Furthermore, after a new type / batch of slurry is introduced into the factory, the dispersion scoring model can be retrained using the sample feature data and dispersion obtained during the mixing of the new slurry using a mixing device, ensuring the model's generalization ability.

[0080] In some embodiments, the input parameters of the dispersion scoring model described above can be electrical parameters. Accordingly, the sample data may exclude sample feature data but include sample electrical parameters. For details, please refer to the relevant description of sample data including sample feature data described above, except that the sample feature data is replaced by sample electrical parameters.

[0081] It should be noted that the electronic device predicts the dispersion of the slurry using a dispersion scoring model. Of course, the electronic device can also directly predict the dispersion of the slurry using the algorithm corresponding to the dispersion scoring model.

[0082] The previous section introduced the process by which electronic equipment predicts the dispersion of slurry. After obtaining the slurry dispersion, if it does not meet the requirements, the electronic equipment can take timely measures to dynamically adjust the dispersion, thereby achieving closed-loop control. The closed-loop control process will be further described below.

[0083] In the event of a predicted dispersion anomaly, an anomaly handling operation is performed; wherein the anomaly handling operation includes adjusting the operating parameters of the stirring motor and / or adjusting the amount of solvent added to the slurry.

[0084] Therefore, when the dispersion is abnormal, the electronic equipment can perform abnormal handling operations to adjust the operating parameters of the stirring motor and the amount of solvent added to the slurry, so that the dispersion of the slurry meets the requirements. This allows for timely adjustment of the slurry dispersion and avoids the slurry from becoming unusable. Furthermore, by adjusting the operating parameters of the stirring motor, it can be made to operate at appropriate parameters, achieving dynamic adjustment of the stirring motor instead of always operating at fixed parameters. This ensures operational flexibility and avoids waste of resources.

[0085] Optionally, when adjusting the amount of solvent added to the slurry, the electronic device can control the adjustment via a regulating valve.

[0086] Taking the above-mentioned abnormal handling operations, including adjusting the operating parameters of the stirring motor and adjusting the amount of solvent added to the slurry, as an example, the process of performing abnormal handling operations in the case of predicted abnormal dispersion may include:

[0087] If the predicted dispersion is below a first threshold, characteristic data of the slurry is determined based on the characteristic data of the stirring motor; wherein the characteristic data is determined based on the electrical parameters of the stirring motor; target operating parameters of the stirring motor and target solvent addition amount corresponding to the characteristic data of the slurry are determined; the stirring motor is controlled according to the target operating parameters to adjust the parameters of the stirring motor to the target operating parameters; and solvent is added to the slurry according to the target addition amount, wherein the amount of solvent added is the target addition amount.

[0088] Therefore, when the slurry dispersion is low, the characteristic data of the stirring motor can be used to determine the current characteristics of the slurry. Then, the operation of the stirring motor is controlled based on the target operating parameters of the stirring motor that match the current characteristics, and solvent is added to the slurry according to the target addition amount of the stirring motor that matches the current characteristics to improve the dispersion of the slurry.

[0089] Optionally, the electronic device can look up the operating parameters of the stirring motor and the amount of solvent added corresponding to the characteristic data of the slurry from a preset data table, and use the found operating parameters as the target operating parameters and the found amount of solvent added as the target amount of solvent added.

[0090] Optionally, the aforementioned operating parameters may include one or more of power, rotational speed, and the rotational speed ratio between the revolution shaft and the rotational shaft. Taking power as an example, the preset data table may include the correspondence between the slurry's characteristic data (such as viscosity) and viscosity and power, respectively. Based on this, the electronic device can look up the target power corresponding to the current viscosity of the slurry from the preset data table to adjust the power of the stirring motor to the target power. Correspondingly, the preset data table may be a power-solid content mapping table. Additionally, optionally, since there is a relationship between power and rotational speed (as described below, P∝N), b2 Therefore, electronic devices can adjust the speed of the stirring motor to adjust the power of the stirring motor.

[0091] Furthermore, the electronic equipment can adjust the power of the stirring motor through a frequency converter, and combine it with a PID control algorithm to achieve a dynamic balance between power and dispersion, thus avoiding overload.

[0092] Optionally, the revolution axis and the rotation axis can be driven by independent frequency converters. The electronic equipment can dynamically adjust the revolution / rotation speed ratio (range 1:2 to 1:5) according to power feedback to achieve multi-axis coordinated control and optimize the slurry flow field distribution.

[0093] Optionally, the solvent mentioned above can be N-methylpyrrolidine (NMP) solvent.

[0094] In some embodiments, the characteristic data of the slurry can be determined by a first model. The electronic device can determine the characteristic data of the slurry based on the characteristic data and the first model; wherein, the first model represents the correlation between the characteristic data of the stirring motor and the characteristic data of the slurry.

[0095] Based on this, the characteristic data of the slurry is calculated by using a first model that reflects the correlation between the characteristic data of the stirring motor and the characteristic data of the slurry, and the current characteristic data of the stirring motor. This enables the automatic and rapid determination of the characteristic data of the slurry without the need for sampling or determination by related testing equipment.

[0096] In some embodiments, the characteristic data of the slurry includes the viscosity of the slurry. Accordingly, the first model may be an exponential relationship model (or dynamic correlation model) between the average current and the slurry viscosity, which represents the correlation between the average current of the stirring motor and the viscosity of the slurry.

[0097] For example, the exponential relationship model between the average current and the slurry viscosity can be I. avg =k1·μ b1 Where k1 represents the proportionality coefficient, which is determined by equipment parameters (such as motor efficiency and blade shape) and environmental conditions (such as temperature and solvent evaporation), and needs to be calibrated according to specific operating conditions. b1 represents the preset value, which can be set according to the actual experiment, such as b1 being 0.78.

[0098] In other embodiments, the first model may be a power-viscosity relationship model, which represents the correlation between the power of the stirring motor and the viscosity of the slurry.

[0099] For example, the power-viscosity relationship model can be P = k²·μ·N b2 ·D b3 .

[0100] Where P represents the power of the stirring motor.

[0101] k2 also represents the proportionality coefficient, which is determined based on the type of stirring motor (or alternatively, the type of stirring equipment) and the type of slurry.

[0102] μ represents viscosity. It reflects the flow resistance of the slurry; the higher the viscosity, the greater the energy required for stirring.

[0103] N represents rotational speed. The power relationship of rotational speed indicates its significant impact on power; higher rotational speeds will greatly increase energy consumption. Correspondingly, b2 is a value greater than 1, such as b2 being 3.

[0104] D represents the blade diameter. The power of the blade diameter indicates the power amplification effect of the blade size. Correspondingly, b3 is a value greater than 1, such as b3 being 5.

[0105] It is understandable that the power-viscosity relationship model described above actually represents the quantitative relationship between the power (P) of the stirring motor and the slurry viscosity (μ), stirring speed (N), and impeller diameter (D).

[0106] Therefore, considering that the current of the stirring motor can reflect the viscosity of the slurry, the viscosity of the slurry can be determined by the current of the stirring motor. Alternatively, considering that the power of the stirring motor can reflect the viscosity of the slurry, the viscosity of the slurry can be determined by the power of the stirring motor.

[0107] In this embodiment, the electronic device can detect the dispersion of the slurry in real time or periodically, thereby adjusting the operating parameters of the stirring motor based on the dispersion to reduce the viscosity of the slurry, thereby improving the dispersion of the slurry and avoiding overload shutdown of high-viscosity slurries (e.g., 70% solid content). Furthermore, since the operating parameters are matched with the viscosity of the slurry, the rationality of the adjustment of the operating parameters is ensured, thereby achieving energy saving, such as an energy saving rate of over 40%.

[0108] It is understandable that when the dispersion is low, the slurry may be unevenly dispersed. Local high viscosity may cause the electronic equipment to predict that the dispersion of the slurry is low. Therefore, the electronic equipment can improve the uniformity of the slurry by increasing the power or speed of the stirring motor, so that the stirring motor can output more torque.

[0109] It should be noted that the above example illustrates how electronic equipment can improve slurry dispersion by adjusting the operating parameters of the stirring motor (e.g., increasing power) and / or adjusting the amount of solvent added (e.g., increasing the degree of solvent addition). Furthermore, abnormal dispersion can also occur when the dispersion is high, i.e., the predicted dispersion of the slurry exceeds a second threshold. In such cases, electronic equipment can also reduce slurry dispersion by adjusting the operating parameters of the stirring motor (e.g., decreasing power) and / or adjusting the amount of solvent added (e.g., decreasing the degree of solvent addition).

[0110] The process of reducing the dispersion of the slurry when the dispersion is high is similar to the process of increasing the dispersion of the slurry mentioned earlier, and will not be repeated here.

[0111] In some embodiments, after obtaining the target operating parameters, the operating parameters in the process parameter library (i.e., the operating parameter library) can also be updated. For example, the electronic device can update the operating parameters corresponding to the type of slurry with predicted dispersion in the process parameter library to the target operating parameters. Thus, when this type of slurry needs to be stirred again, the electronic device can control the stirring motor to operate with the target operating parameters, reducing the possibility of abnormal slurry dispersion.

[0112] In some embodiments, the electronic device can also provide early warning. During the operation of the stirring motor, if the stirring motor meets preset process abnormality conditions, it indicates that a process abnormality may occur. Therefore, the operating parameters of the stirring motor can be adjusted to achieve timely adjustment of the operating parameters, realize process abnormality early warning (i.e., realize first-level early warning), and avoid serious process abnormalities in the stirring motor.

[0113] During the operation of the mixing motor, if the mixing motor meets the preset mechanical fault conditions, it indicates that the mixing motor may have a mechanical fault (i.e., a two-level shutdown warning is implemented). Therefore, the mixing motor can be controlled to stop running, thus realizing a mechanical fault warning and avoiding serious mechanical faults in the mixing motor.

[0114] Optionally, the aforementioned preset process anomaly conditions include the power fluctuation variance of the stirring motor being greater than or equal to a preset variance threshold; wherein the preset variance threshold is determined based on historical power fluctuation variances. Therefore, when the power fluctuation variance of the stirring motor is greater than or equal to the preset variance threshold, it indicates that the stirring motor is operating abnormally, and the likelihood of a process anomaly is high; thus, a process anomaly warning can be issued.

[0115] Furthermore, the aforementioned preset variance threshold can be preset to M times the mean of the power fluctuation variance over a historical period. M is a positive number greater than 1. For example, if the mean of the power fluctuation variance over a preset historical period is 50 kW², then the preset variance threshold can be 50 * 2 = 100 kW².

[0116] Optionally, the preset mechanical fault conditions include one or more of the following: the high-frequency noise energy of the mixing motor is greater than or equal to a preset time; the remaining life of the mixing motor bearing is less than a preset life; or the peak current ratio of the mixing motor is greater than a preset current ratio. Based on this, when the high-frequency noise energy of the mixing motor is relatively long, the remaining life of the mixing motor bearing is relatively short, and the peak current ratio of the mixing motor is relatively large, it indicates that the mixing motor may fail, thus enabling a mechanical fault early warning system.

[0117] Optionally, the high-frequency noise energy in the aforementioned preset mechanical fault conditions can be high-frequency noise energy greater than 5V / Hz. Correspondingly, the aforementioned preset time can be 5 minutes.

[0118] It should be noted that the dispersion scoring model described above may or may not be trained on electronic devices, and this application does not impose any restrictions on it.

[0119] The following will illustrate a possible implementation process of the above dispersion prediction method with a specific example. Figure 3 As shown, the process includes the following steps:

[0120] S1. The electronic device receives the stirring start command input by the user.

[0121] S2. In response to the stirring start command, the electronic device determines whether there are operating parameters in the operating parameter library that correspond to the type of slurry to be stirred.

[0122] If the operating parameters corresponding to the type of slurry to be mixed are available, the electronic equipment may execute S3. Otherwise, the electronic equipment may execute S4.

[0123] S3. The electronic equipment sets the operating parameters of the mixing motor of the mixing equipment to the operating parameters corresponding to the type of slurry to be mixed.

[0124] S4. The electronic equipment sets the operating parameters of the stirring motor of the stirring equipment to the initial operating parameters.

[0125] S5. Electronic equipment controls the stirring motor to operate according to the set operating parameters.

[0126] The electronic device triggers the stirring motor to start running, causing the stirring equipment to begin mixing the slurry.

[0127] S6. The electronic device receives the electrical parameters of the stirring motor collected by the data acquisition module.

[0128] After stirring begins, the data acquisition module can collect the electrical parameters of the stirring motor and send the collected electrical parameters to the electronic equipment in real time or periodically.

[0129] S7. Electronic equipment uses a low-pass filter to filter electrical parameters.

[0130] S8. The electronic device extracts features from the processed electrical parameters to obtain frequency domain feature data and time domain feature data.

[0131] S9. The electronic device inputs the frequency domain feature data and time domain feature data into the dispersion scoring model to obtain the predicted dispersion of the slurry.

[0132] Electronic equipment can predict the dispersion of slurry in real time or periodically, thus enabling timely detection of dispersion without having to wait until the mixing is finished.

[0133] The process of using electronic equipment to predict the dispersion of slurry has been described above. The following section describes the process of using electronic equipment to adjust the dispersion in cases of abnormal dispersion.

[0134] S10. In case of abnormal dispersion, the electronic device adjusts the operating parameters of the stirring motor and the amount of solvent added.

[0135] When the dispersion does not meet the requirements, electronic equipment can utilize PID algorithms and automatic solvent compensation mechanisms to achieve closed-loop regulation of slurry dispersion, ensuring slurry quality, thus avoiding slurry instability and improving the efficiency of the mixing process. Specifically, the PID algorithm can adjust the flow rate of the liquid delivery pump.

[0136] Optionally, upon receiving a user-inputted command to end mixing, the electronic device can trigger the mixing motor to stop operating in response to the command; in other words, it can trigger the mixing equipment to stop mixing, thus ending the mixing process. Alternatively, the electronic device can also trigger the mixing equipment to stop mixing when the slurry dispersion meets the standard.

[0137] The process of adjusting the dispersion has been described above. The following section will explain how to issue an early warning system when an abnormality occurs during the operation of the mixing motor.

[0138] S11. During the operation of the stirring motor, if the stirring motor meets the preset abnormal process conditions, the electronic equipment adjusts the operating parameters of the stirring motor.

[0139] S12. During the operation of the mixing motor, if the mixing motor meets the preset mechanical fault conditions, the electronic equipment controls the mixing motor to stop running.

[0140] In this embodiment, the electronic device can collect high-frequency noise energy (or alternatively, high-frequency signal energy, such as signal energy in the >200Hz band) through a high-frequency noise detection unit. This energy is used to distinguish between mechanical faults (such as bearing wear) and process abnormalities, triggering a multi-level early warning mechanism to differentiate between mechanical and process abnormalities. This allows for targeted resolution of the abnormalities and reduces unplanned downtime. The multi-level early warning mechanism includes a first-level warning and a second-level warning. For example, a first-level warning refers to adjusting the speed of the stirring motor when the power fluctuation variance exceeds the historical average of 2σ; a second-level shutdown refers to controlling the stirring motor to stop operation when a mechanical impact characteristic frequency band is detected.

[0141] Alternatively, the electronic device may output a warning message when issuing an early warning. For example, if the stirring motor meets preset process abnormality conditions, the electronic device may output a process abnormality message. If the stirring motor meets preset mechanical fault conditions, the electronic device may output a mechanical fault message.

[0142] In this embodiment, when the slurry is normally dispersed, the main frequency of the power spectrum of the stirring motor is concentrated at the fundamental frequency (e.g., 50Hz). When the energy of low-frequency harmonics (<10Hz) increases, it indicates an increase in agglomeration resistance. When the amplitude of high-frequency noise (>200Hz) increases, the possibility of mechanical wear is greater.

[0143] In some embodiments, the electronic device can dynamically adjust the speed of the stirring motor according to the dispersion stage of the slurry. For example, in the initial stage of stirring (the particle agglomeration stage of the slurry), the stirring motor can be controlled to operate at a high speed to increase the shear force. In the middle stage of stirring (the uniform dispersion stage of the slurry), the stirring motor can be controlled to operate at a medium speed to balance energy consumption. In the later stage of stirring (the stabilization stage of the slurry), the stirring motor can be controlled to operate at a low speed to prevent overload.

[0144] In this embodiment, the electronic device can dynamically compensate for the solvent based on the predicted dispersion and thresholds (such as the first threshold and the second threshold mentioned above), effectively controlling the dispersion error and the coefficient of variation (CV) value of the slurry rheological properties, and reducing energy consumption through a variable frequency speed control strategy. Additionally, the electronic device can also display a dispersion trend curve.

[0145] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0146] For example, Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown. Figure 4 As shown, the electronic device 500 may include a processor 510. Optionally, the electronic device 500 may also include a memory 520. Exemplarily, the device is a computing device.

[0147] Processor 510 may include one or more processing units, such as application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0148] The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0149] The processor 510 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 510 is a cache memory. This memory can store instructions or data that the processor 510 has just used or that are used repeatedly. If the processor 510 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 510, and thus improves the efficiency of the system.

[0150] In some embodiments, processor 510 may include one or more interfaces. These one or more interfaces may be used to connect processor 510 to memory 520.

[0151] The memory 520 can be used to store computer executable program code, which includes instructions. The memory 520 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as image playback), etc. The data storage area may store data created during the use of the electronic device 500, etc. The processor 510 executes various functional applications and data processing of the electronic device 500 by running instructions stored in the memory 520 and / or instructions stored in memory located within the processor.

[0152] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the device. In other embodiments of the present invention, the device may include... Figure 4 The diagram shows more or fewer components, or combinations of components, or separate components, or different arrangements of components. The components shown can be implemented in hardware, software, or a combination of both.

[0153] like Figure 5 The diagram shown is a structural schematic of an electronic device provided in an embodiment of the present invention. This electronic device 2200 can be used to implement the methods described in the above method embodiments. For example, the electronic device 2200 may specifically include a processing unit 2201.

[0154] The processing unit 2201 is used to support the electronic device 2200 in performing operations. Figures 1 to 3 The processing function described in any one of the following.

[0155] Optional, Figure 5 The illustrated electronic device 2200 may also include a communication unit ( Figure 5 (Not shown in the image), this communication unit is used to support the electronic device 2200 in performing the steps of communication between the electronic device and other devices in the embodiments of the present invention.

[0156] Optional, Figure 5 The illustrated electronic device 2200 may further include a storage unit 2203 that stores programs or instructions. When the processing unit 2201 executes the program or instructions, it causes... Figure 5 The electronic device 2200 shown can perform the method described in the above-described method embodiments.

[0157] Figure 5 The technical effects of the electronic device 2200 shown can be referred to the technical effects of the method shown in the above method embodiments, and will not be repeated here. Figure 5 The processing unit 2201 involved in the illustrated electronic device 2200 can be implemented by a processor or processor-related circuit components, and can be a processor or a processing module. The communication unit can be implemented by a transceiver or transceiver-related circuit components, and can be a transceiver or a transceiver module.

[0158] This invention also provides a chip system, such as... Figure 6 As shown, the chip system includes at least one processor 2301 and at least one interface circuit 2302. The processor 2301 and the interface circuit 2302 are interconnected via lines. For example, the interface circuit 2302 can be used to receive signals from other devices. As another example, the interface circuit 2302 can be used to send signals to other devices (e.g., the processor 2301). Exemplarily, the interface circuit 2302 can read instructions stored in memory and send those instructions to the processor 2301. When the instructions are executed by the processor 2301, the device can perform the various steps executed by the device in the above embodiments. Of course, the chip system may also include other discrete components, and this embodiment of the invention does not specifically limit this.

[0159] Optionally, the chip system may contain one or more processors. These processors can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0160] Optionally, the chip system may contain one or more memories. These memories may be integrated with the processor or separated from it; this invention is not limiting. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed on different chips. This invention does not specifically limit the type of memory or the arrangement of the memory and processor.

[0161] For example, the chip system may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0162] In some embodiments, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described.

[0163] In some embodiments, this application provides a computer program product including a computer program that, when executed by a processor, implements the method described above.

[0164] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The method steps disclosed in the embodiments of the present invention can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0165] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A dispersion prediction method, characterized in that, include: During the mixing of the slurry by the mixing motor, the electrical parameters of the mixing motor are acquired; Feature extraction is performed on the electrical parameters to obtain feature data; wherein, the feature data includes time-domain feature data and frequency-domain feature data; the time-domain feature data includes the mean current and the power fluctuation variance; the frequency-domain feature data includes the main frequency amplitude and the proportion of harmonic energy. The feature data is input into the dispersion scoring model to obtain the predicted dispersion of the slurry; wherein, the dispersion scoring model is... a1, a2, a3, and a4 are all preset values, P var Iavg represents the power fluctuation variance, and Iavg represents the mean current. f0 The amplitude of the main frequency, Af 1-5 / A f0 This indicates the proportion of harmonic energy.

2. The method according to claim 1, characterized in that, After obtaining the predicted dispersion corresponding to the slurry, the method further includes: In the event of an abnormal predicted dispersion, an anomaly handling operation is performed; wherein the anomaly handling operation includes adjusting the operating parameters of the stirring motor and / or adjusting the amount of solvent added corresponding to the slurry.

3. The method according to claim 2, characterized in that, In the event of an anomaly in the predicted dispersion, the anomaly handling operation includes: If the predicted dispersion is lower than a first threshold, the characteristic data of the slurry are determined based on the feature data. Determine the target operating parameters of the stirring motor and the target amount of solvent to be added, corresponding to the characteristic data of the slurry; Control the stirring motor according to the target operating parameters; The solvent is added to the slurry according to the target addition amount.

4. The method according to claim 3, characterized in that, The feature data also includes the power of the stirring motor; determining the characteristic data of the slurry based on the feature data includes: Based on the aforementioned feature data and in conjunction with the first model, the characteristic data of the slurry are determined; wherein, the characteristic data of the slurry includes the viscosity of the slurry; The first model represents the correlation between the average current and the viscosity of the slurry; Alternatively, the first model represents the relationship between the power of the stirring motor and the viscosity of the slurry.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: During the operation of the stirring motor, if the stirring motor meets the preset abnormal process conditions, the operating parameters of the stirring motor are adjusted.

6. The method according to claim 5, characterized in that, The preset process abnormality conditions include the power fluctuation variance of the stirring motor being greater than or equal to a preset variance threshold; wherein the preset variance threshold is determined based on historical power fluctuation variance.

7. The method according to any one of claims 1 to 4, characterized in that, The method further includes: During the operation of the stirring motor, if the stirring motor meets the preset mechanical fault conditions, the stirring motor is controlled to stop operating.

8. The method according to claim 7, characterized in that, The preset mechanical failure conditions include one or more of the following: the high-frequency noise energy of the stirring motor is greater than or equal to a preset time; the remaining life of the bearing of the stirring motor is less than a preset life; or the peak current ratio of the stirring motor is greater than a preset current ratio.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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