A lithium battery slurry viscosity prediction method, system and electronic device thereof

Abstract

The present application belongs to the technical field of lithium battery slurry concentration measurement, and relates to a lithium battery slurry viscosity prediction method, system and electronic equipment thereof, comprising the following steps: collecting operating parameter data of a stirrer of target slurry in a stirring process in a preset time period; obtaining actual viscosity of the target slurry in the preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operating parameter data of the stirrer to obtain a first predicted viscosity of the target slurry; determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; obtaining a prediction time period in a real-time prediction request, and predicting a second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model. The present application has the advantages that the viscosity of the slurry in the stirrer can be accurately predicted, and the situation that the viscosity of the slurry is too high or too low during stirring can be avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium battery slurry concentration measurement, and relates to a lithium battery slurry viscosity prediction method and system and electronic equipment thereof. BACKGROUND

[0002] The positive electrode slurry and the negative electrode slurry are one of important raw materials for manufacturing positive electrodes and negative electrodes of lithium ion batteries. The positive electrode slurry is usually composed of a binder, a conductive agent and a positive electrode material, and the negative electrode slurry is composed of a binder and graphite carbon powder. The preparation of the positive electrode slurry and the negative electrode slurry includes a series of processes such as mixing, dissolving and dispersing between liquids and liquids or between liquids and solid materials, and the whole process is accompanied by changes in temperature, viscosity, environment and the like. In the positive electrode slurry and the negative electrode slurry, the dispersibility and uniformity of the granular active material directly affect the movement of lithium ions between the two poles of the battery, and therefore the mixing and dispersion of the slurry of the electrode material, i.e. the quality of the slurry mixing, is crucial in the production of lithium ion batteries.

[0003] The slurry viscosity is an important characterization index of the quality of the slurry mixing. The slurry viscosity itself does not affect the performance of the battery, but the viscosity has a great influence on the stability of the slurry and the subsequent coating process. When the slurry viscosity is high, the particles are not easy to settle, and the stability and uniformity of the slurry are relatively good, but too high viscosity will lead to poor flowability of the slurry, affecting the coating effect. Of course, too low viscosity is also not acceptable, and too low viscosity will cause poor stability of the slurry, particle agglomeration, difficulty in drying during coating, and problems such as cracking of the coating layer and inconsistency of the surface density. Therefore, the size of the slurry viscosity will directly affect the quality of the battery electrode coating, and therefore it is particularly important to obtain the slurry viscosity data in time and accurately when performing the slurry mixing operation. However, the current method for testing the slurry viscosity is to take a sample after each batch of slurry mixing is completed and test a viscosity value on a rotary viscometer. This method has the following defects: ① the sample can only be taken and tested after the slurry mixing is completed, and if the viscosity is out of standard, the process cannot be adjusted in time; ② two people are needed to cooperate for each test, one person operates the equipment to open the tank, and the other person takes the sample for testing, which is time-consuming and labor-intensive, and also has a certain safety risk; ③ the slurry after testing cannot be returned to the mixing tank in time for stirring, which is easy to agglomerate, and frequent opening of the tank also increases the risk of foreign matter falling into the mixing tank. In summary, the existing detection of the lithium battery slurry viscosity is not convenient and has a high detection cost. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a lithium battery slurry viscosity prediction method, system and electronic equipment thereof, which can accurately predict the viscosity of the slurry in the mixer, avoid the occurrence of too high or too low viscosity of the slurry during stirring, and thus improve the effect of the lithium battery during coating.

[0005] In order to achieve the above purpose, the following technical solutions are adopted in the present application:

[0006] The first aspect of the present application provides a lithium battery slurry viscosity prediction method, comprising the following steps:

[0007] Collecting running parameter data of a stirrer of the target slurry during stirring in a preset time period, wherein the running parameter data of the stirrer comprises rotation speed, torque, current, power and temperature;

[0008] Obtaining actual viscosity of the target slurry in the preset time period, and calculating viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the running parameter data of the stirrer to obtain first predicted viscosity of the target slurry;

[0009] Determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;

[0010] Obtaining a prediction time period in a real-time prediction request, and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

[0011] Further, the slurry viscosity prediction model is trained according to the following steps:

[0012] Inputting the running parameter data of the stirrer in the training data into the slurry viscosity prediction model, and outputting slurry viscosity prediction data by the slurry viscosity prediction model, wherein the training data comprises multiple groups of model training data, and each group of model training data comprises running parameter data of a stirrer and actual viscosity data corresponding to the target slurry;

[0013] Adjusting model parameters of the slurry viscosity prediction model according to the actual viscosity data corresponding to the running parameter data of the stirrer and the slurry viscosity prediction data, and continuing to perform the step of inputting the running parameter data of the stirrer in the training data into the slurry viscosity prediction model until a preset training condition is met to obtain a trained slurry viscosity prediction model.

[0014] Further, the slurry viscosity prediction model is:

[0015] y=f(R,X k ,X k-1 ,X k-2 ,t);

[0016] wherein y is the first predicted viscosity of the target slurry, R is a formula of the target slurry, X k is the running parameter data of the stirrer at the kth sampling, X k-1 is the running parameter data of the stirrer at the previous time of the kth sampling, and X k-2is the running parameter data of the previous mixer at the k-1th sampling time, and t is the sampling time interval.

[0017] Further, the running parameter data of the mixer during the stirring process of the target slurry in the preset time period comprises: the running parameter data of the mixer at different time instants in multiple same time intervals in the preset time period.

[0018] Further, the viscosity of the target slurry is detected by a rheometer to obtain the actual viscosity of the target slurry at multiple different collection time instants in the preset time period.

[0019] The second aspect of the present application provides a lithium battery slurry viscosity prediction system, comprising:

[0020] The collection module is configured to collect running parameter data of a mixer during a stirring process of a target slurry in a preset time period, wherein the running parameter data of the mixer comprises: rotational speed, torque, current, power and temperature.

[0021] The calculation module is configured to obtain an actual viscosity of the target slurry in the preset time period, and calculate the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the running parameter data of the mixer, so as to obtain a first predicted viscosity of the target slurry.

[0022] The determination module is configured to determine a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity.

[0023] The viscosity prediction module is configured to obtain a prediction time period in a real-time prediction request, and predict a second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

[0024] The third aspect of the present application provides an electronic device, comprising: the electronic device comprises a memory, a processor and a lithium battery slurry viscosity prediction program stored in the memory and executable on the processor, and the lithium battery slurry viscosity prediction program realizes the steps of the lithium battery slurry viscosity prediction method when executed by the processor.

[0025] The present application has the following beneficial effects:

[0026] (1), by collecting the running parameter data of the stirrer of the target slurry in the stirring process in a preset time period, the running parameter data of the stirrer includes: speed, torque, current, power and temperature; the actual viscosity of the target slurry in the preset time period is obtained, and the viscosity of the target slurry is calculated based on the pre-established slurry viscosity prediction model according to the running parameter data of the stirrer, to obtain the first predicted viscosity of the target slurry; the correction coefficient of the slurry viscosity prediction model is determined according to the actual viscosity and the first predicted viscosity; the prediction time period in the real-time prediction request is obtained, and the second predicted viscosity of the target slurry in the prediction time period is predicted based on the correction coefficient and the slurry viscosity prediction model; so that the viscosity of the slurry in the stirrer can be accurately predicted when the stirrer is stirring the slurry, and the situation that the viscosity of the slurry is too high or too low during stirring is avoided, thereby improving the effect of the lithium battery during coating.

[0027] (2), since the concentration of the slurry in the stirrer can be accurately predicted, the situation that the energy consumption and cost increase due to the overlong stirring time and the overlong slurry preparation process of the lithium battery can be avoided. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a flowchart of the prediction method in the present application; Figure 1

[0029] Figure 2 is a structural diagram of the prediction system in the present application; Figure 2

[0030] Figure 3 is a structural diagram of the electronic device in the present application. Figure 3 DETAILED DESCRIPTION

[0031] The embodiments of the present application will be described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0032] With reference to the accompanying drawings, Figure 1 the first aspect of the present application is to provide a lithium battery slurry viscosity prediction method, comprising the following steps:

[0033] S100: collecting the running parameter data of the stirrer of the target slurry in the stirring process in a preset time period, the running parameter data of the stirrer includes: speed, torque, current, power and temperature.

[0034] ​​​It should be noted that, since the movement of the mixer is driven by the frequency converter, the frequency converter is internally controlled by the MCU, and the frequency converter has communication capability, therefore the frequency converter communication is accessed to the PLC controller, and the corresponding sensing value is synchronized to the corresponding register of the PLC at any time. Then read through the communication protocol of the PLC. It should be understood that, in the running parameter data of the mixer, the current is the actual output current of the mixer frequency converter, the power is the actual output power of the mixer frequency converter, and the temperature is the temperature measured by the temperature sensor inside the motor of the mixer.

[0035] S200: obtaining the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on the running parameter data of the mixer according to a pre-established slurry viscosity prediction model to obtain a first predicted viscosity of the target slurry.

[0036] S300: determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity.

[0037] S400: obtaining a prediction time period in a real-time prediction request, and predicting a second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

[0038] By collecting the running parameter data of the mixer during the stirring process of the target slurry in a preset time period, the running parameter data of the mixer includes: speed, torque, current, power and temperature; obtaining the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on the running parameter data of the mixer according to a pre-established slurry viscosity prediction model to obtain a first predicted viscosity of the target slurry; determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; obtaining a prediction time period in a real-time prediction request, and predicting a second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model; so that the viscosity of the slurry in the mixer can be accurately predicted when the mixer is stirring the slurry, avoiding the situation that the viscosity of the slurry is too high or too low during stirring, thereby improving the effect of the lithium battery during coating.

[0039] In one embodiment, the slurry viscosity prediction model is trained according to the following steps:

[0040] The running parameter data of the mixer in the training data is input into the slurry viscosity prediction model, and the slurry viscosity prediction data is output through the slurry viscosity prediction model, wherein the training data includes multiple groups of model training data, and each group of model training data includes: running parameter data of the mixer and corresponding actual viscosity data of the target slurry;

[0041] According to the actual viscosity data corresponding to the operation parameter data of the mixer and the slurry viscosity prediction data, the model parameters of the slurry viscosity prediction model are adjusted, and the step of inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model is continuously performed until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.

[0042] It should be noted that the slurry viscosity prediction model in the above embodiment is a three-layer neural network model. By setting the slurry viscosity prediction model as a three-layer neural network model, the accuracy of the slurry viscosity prediction model can be improved.

[0043] In one embodiment, the slurry viscosity prediction model is:

[0044] y=f(R,X k ,X k-1 ,X k-2 ,t);

[0045] wherein y is the first predicted viscosity of the target slurry, R is the formula of the target slurry, X k is the operation parameter data of the mixer at the kth sampling, X k-1 is the operation parameter data of the mixer at the previous time of the kth sampling, X k-2 is the operation parameter data of the mixer at the previous time of the k-1th sampling, and t is the sampling time interval. By setting the slurry viscosity prediction model as y=f(R,X k ,X k-1 ,X k-2 ,t), the sampling results of the previous two times at the prediction time point are also added to the slurry viscosity prediction model when predicting the concentration of the target slurry, thereby improving the accuracy of the slurry viscosity prediction model.

[0046] In one embodiment, the operation parameter data of the mixer during the stirring process of the target slurry in the preset time period includes: collecting the operation parameter data of the mixer at multiple different time intervals at different time points in the preset time period.

[0047] In one embodiment, the viscosity of the target slurry is detected by a rheometer to obtain the actual viscosity of the target slurry at multiple different collection time points in the preset time period.

[0048] In one embodiment, the function expression of the correction coefficient of the actual viscosity of the target slurry is:

[0049]

[0050] wherein y0 is the actual viscosity of the target slurry, is the predicted viscosity of the target slurry.

[0051] Reference is made to the accompanying drawings Figure 2 The second aspect of the present application provides a lithium battery slurry viscosity prediction system, comprising:

[0052] A collection module is configured to collect running parameter data of a stirrer during stirring of target slurry in a preset time period, wherein the running parameter data of the stirrer includes rotation speed, torque, current, power and temperature.

[0053] A calculation module is configured to obtain actual viscosity of the target slurry in the preset time period, and calculate viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the running parameter data of the stirrer, so as to obtain first predicted viscosity of the target slurry.

[0054] A determination module is configured to determine a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity.

[0055] A viscosity prediction module is configured to obtain a prediction time period in a real-time prediction request, and predict second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

[0056] In one embodiment, the slurry viscosity prediction model is trained according to the following steps:

[0057] The running parameter data of the stirrer in the training data is input into the slurry viscosity prediction model, and slurry viscosity prediction data is output by the slurry viscosity prediction model, wherein the training data includes multiple groups of model training data, and each group of model training data includes running parameter data of a stirrer and actual viscosity data corresponding to target slurry.

[0058] The model parameters of the slurry viscosity prediction model are adjusted according to the actual viscosity data corresponding to the running parameter data of the stirrer and the slurry viscosity prediction data, and the step of inputting the running parameter data of the stirrer in the training data into the slurry viscosity prediction model is continuously performed until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.

[0059] It should be noted that the slurry viscosity prediction model in the above embodiment is a three-layer neural network model, and by taking the slurry viscosity prediction model as a three-layer neural network model, the accuracy of the slurry viscosity prediction model in prediction can be improved.

[0060] In one embodiment, the slurry viscosity prediction model is:

[0061] y=f(R,X k ,X k-1 ,X k-2t);

[0062] wherein y is the first predicted viscosity of the target slurry, R is the recipe of the target slurry, X_k is the running parameter data of the mixer at the kth sampling, X_(k-1) is the running parameter data of the mixer at the previous time of the kth sampling, X_(k-2) is the running parameter data of the mixer at the previous time of the (k-1)th sampling, and t is the time interval of the sampling.

[0063] By setting the slurry viscosity prediction model as y = f(R, X k ,X k-1 ,X k-2 ,t), the sampling results of the previous two times before the prediction time point are also added to the slurry viscosity prediction model when predicting the concentration of the target slurry, so as to improve the accuracy of the prediction of the slurry viscosity prediction model.

[0064] In one embodiment, the collection of the running parameter data of the mixer during the stirring of the target slurry in the preset time period includes: collection of the running parameter data of the mixer at multiple different time instants in the same time interval in the preset time period.

[0065] In one embodiment, the viscosity of the target slurry is detected by a rheometer to obtain the actual viscosity of the target slurry at multiple different collection time instants in the preset time period.

[0066] By setting the collection module, the calculation module, the determination module, and the viscosity prediction module; the calculation module is used to obtain the actual viscosity of the target slurry in the preset time period, and calculate the viscosity of the target slurry based on the running parameter data of the mixer and the pre-established slurry viscosity prediction model to obtain the first predicted viscosity of the target slurry; the calculation module is used to obtain the actual viscosity of the target slurry in the preset time period, and calculate the viscosity of the target slurry based on the running parameter data of the mixer and the pre-established slurry viscosity prediction model to obtain the first predicted viscosity of the target slurry; the determination module is used to determine the correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; the prediction module is used to obtain the prediction time period in the real-time prediction request, and predict the second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model; so that the viscosity of the slurry in the mixer can be accurately predicted when the mixer is stirring the slurry, and the situation of too high or too low viscosity of the slurry during stirring is avoided, thereby improving the effect of the lithium battery during coating.

[0067] Reference is made to the accompanying drawings Figure 3The third aspect of the present application provides an electronic device, comprising: the electronic device comprises a memory, a processor, and a lithium battery slurry viscosity prediction program stored in the memory and executable on the processor, and the lithium battery slurry viscosity prediction program implements the steps of the lithium battery slurry viscosity prediction method when executed by the processor.

[0068] The processor is configured to complete various control logics of the system, and can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine), or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination of these components. In addition, the processor can also be any conventional processor, microprocessor or state machine. The processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP and / or any other such configuration.

[0069] The memory is a non-volatile computer readable storage medium, which can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the lithium battery slurry viscosity prediction method in the embodiments of the present application. The processor executes the non-volatile software programs, instructions and units stored in the memory, thereby performing various functional applications and data processing of the system, i.e. implementing the lithium battery slurry viscosity prediction method in the above method embodiments.

[0070] The memory can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to system use, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device or other non-volatile solid-state storage device. In some embodiments, the memory can optionally include a memory remotely arranged with respect to the processor, and these remote memories can be connected to the system through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0071] One or more units are stored in the memory, and when executed by one or more processors, the lithium battery slurry viscosity prediction method in any of the above method embodiments is executed, for example, the method steps S100 to S400 in the above description. Figure 1

[0072] ​The above-described embodiments are merely illustrative for the present application, wherein the units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus a general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product in essence or in the form of a part of the related technology. The computer software product can exist in a computer readable storage medium, such as a ROM / RAM, a disk, an optical disk, etc., and includes a plurality of instructions used to make a computer electronic device (which can be a personal computer, a server, or a network electronic device, etc.) execute the methods of the embodiments or some parts of the embodiments.

[0074] Conditional language such as, among others, "can," "could," "might" or "may," unless specifically stated otherwise, generally are intended to convey that a certain feature, element or process can or can not be included in some implementations. Thus, such conditional language is not generally intended to imply that a feature, element or process is required in one or more implementations or that a feature, element or process is necessary or indispensable for one or more implementations. Such conditional language limitations generally are only used to reflect optional implementation aspects of the described implementations.

[0075] What has been described herein in the specification and drawings includes examples of a lithium battery slurry viscosity prediction method, system, and electronic device thereof. Of course, not every conceivable combination of elements and / or method is described herein, but one of ordinary skill in the art will recognize that many other combinations are possible. Accordingly, it is expressly intended that the claims not be limited to the descriptio n herein, but rather are intended to cover all modifications that would be apparent to one of ordinary skill in the art upon reading the description herein, and many alternatives, modifications, additions and deletions will be apparent to those skilled in the art. Thus, the scope should be accorded the broadest interpretation so as to encompass all such modifications and alternative forms. All patents, patent applications, publications, and descriptions mentioned herein are incorporated by reference in their entirety for the disclosure provided herein. The foregoing description details specific embodiments of the application. It will be appreciated, however, that no matter how detailed the above descriptions are, they are still only the specific embodiments of the present application. Hence, specific embodiments can not include all of the features that are described above, or can include one or more additional features that are not described above. Moreover, it will be appreciated that the language used herein has been principally selected for readability and instructional purposes and can not have been selected to delineate or circumscribe the patent rights. It is therefore intended that the scope of the application be limited only by the broadest interpretable concept underlying the essential characteristics and novel combinations of parts, elements and / or steps recited in each claim.

[0076] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements are not the essence of the corresponding technical solutions, which deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for predicting the viscosity of lithium battery slurry, characterized in that, The method comprises the following steps: Collecting running parameter data of the stirring machine during stirring of the target slurry in a preset time period, the running parameter data of the stirring machine comprising: rotating speed, torque, current, power and temperature; Obtaining actual viscosity of the target slurry in the preset time period, and calculating viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the running parameter data of the stirring machine, to obtain first predicted viscosity of the target slurry; Determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; Obtaining a prediction time period in a real-time prediction request, and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

2. The method for predicting the viscosity of lithium battery slurry according to claim 1, characterized in that, The slurry viscosity prediction model is trained according to the following steps: Inputting the running parameter data of the stirring machine in the training data into the slurry viscosity prediction model, and outputting slurry viscosity prediction data by the slurry viscosity prediction model, wherein the training data comprises multiple groups of model training data, and each group of model training data comprises: running parameter data of the stirring machine and actual viscosity data corresponding to the target slurry; Adjusting model parameters of the slurry viscosity prediction model according to the actual viscosity data corresponding to the running parameter data of the stirring machine and the slurry viscosity prediction data, and continuing to perform the step of inputting the running parameter data of the stirring machine in the training data into the slurry viscosity prediction model until a preset training condition is met, to obtain a trained slurry viscosity prediction model.

3. The method for predicting the viscosity of lithium battery slurry according to claim 1, characterized in that, The slurry viscosity prediction model is: y = f(R, X k ,X k-1 ,X k-2 ,t); where y is the first predicted viscosity of the target slurry, R is the recipe of the target slurry, X k is the operating parameter data of the blender at the kth sampling, X k-1 is the operating parameter data of the blender at the previous sampling of the kth sampling, X k-2 is the operating parameter data of the blender at the previous sampling of the k-1th sampling, and t is the sampling interval.

4. The method for predicting the viscosity of lithium battery slurry according to claim 1, characterized in that, The collecting of the running parameter data of the stirring machine during stirring of the target slurry in a preset time period comprises: collecting running parameter data of the stirring machine at multiple different time intervals at different time points in the preset time period.

5. The method for predicting the viscosity of lithium battery slurry according to claim 4, characterized in that, The viscosity of the target slurry is detected by a rheometer to obtain actual viscosity of the target slurry at multiple different collection time points in the preset time period.

6. A lithium battery slurry viscosity prediction system, characterized by, The method comprises: A collection module is configured to collect running parameter data of a stirring machine during stirring of a target slurry in a preset time period, the running parameter data of the stirring machine comprising: rotating speed, torque, current, power and temperature; A calculation module is configured to obtain actual viscosity of the target slurry in the preset time period, and calculate viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the running parameter data of the stirring machine, to obtain first predicted viscosity of the target slurry; A determination module is configured to determine a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; A viscosity prediction module is configured to obtain a prediction time period in a real-time prediction request, and predict second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.

7. An electronic device, comprising: The method comprises: The electronic device comprises a memory, a processor, and a lithium battery slurry viscosity prediction program stored in the memory and executable on the processor, wherein the lithium battery slurry viscosity prediction program, when executed by the processor, implements the steps of the lithium battery slurry viscosity prediction method according to any one of claims 1-5.

Images