Method and system for improving and optimizing graphitization process parameters of negative electrode material

Abstract

The invention discloses a negative electrode material graphitization process parameter improvement and optimization method and system. The method comprises the following steps: carrying out material loading based on a tandem type multi-cavity crucible; constructing a graphitization prediction model based on the historical production data set; based on the real-time temperature data of each area, the uniform value of the temperature in the crucible is obtained by dynamically controlling the current density; based on the graphitization prediction model, a graphitization process is obtained, and a temperature control instruction is formulated; comparing graphitization real-time process data obtained by a sensor with predicted graphitization process data to obtain an error value, and judging whether to execute a formulating instruction or not; if the error value is large, a temperature control instruction is formulated in real time according to the current graphitization process data, and a production result is fed back to the prediction model for model optimization. The method has the advantages that dynamic temperature control and accurate adjustment are realized by combining the real-time temperature data and the graphitization prediction model, so that the graphitization process is optimized, and the production stability and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to parameter optimization technology, and in particular to a method and system for improving and optimizing graphitization process parameters of negative electrode materials. Background Art

[0002] With the increasing demand for lithium-ion batteries, especially with the widespread adoption of new energy vehicles and consumer electronics, the performance requirements for anode materials are constantly increasing. Therefore, optimizing the graphitization process is particularly important. Optimizing the graphitization process can improve the overall performance of anode materials, enhancing the battery's energy density, cycle stability, and charge-discharge efficiency. Studies have shown that excessively high or low graphitization temperatures can affect the orderliness of the graphite layers, thereby impacting the battery's lifespan and performance. Therefore, finding optimal graphitization conditions by scientifically and rationally adjusting various graphitization process parameters has become a key issue in current scientific research and industrial production.

[0003] Currently, the common methods for optimizing graphitization parameters for negative electrode materials on the market rely primarily on traditional temperature control systems and optimization algorithms based on empirical formulas. These methods typically control the graphitization process by setting fixed temperature ranges and current densities. While these methods can achieve a certain degree of graphitization, they suffer from large temperature fluctuations and low temperature control accuracy in actual applications. Furthermore, traditional methods often lack real-time data feedback mechanisms, making it impossible to dynamically adjust process parameters. They rely solely on pre-defined models and are unable to flexibly adjust to changes in the actual production environment, resulting in low production efficiency and poor product quality stability. Summary of the Invention

[0004] To improve existing methods for optimizing graphitization parameters for negative electrode materials, a method and system for improving graphitization process parameters is provided. This method utilizes real-time temperature data and, in conjunction with a graphitization prediction model, enables dynamic temperature control and precise adjustment, thereby optimizing the graphitization process and improving production stability and efficiency. Through a closed-loop feedback mechanism, the prediction model is continuously optimized to ensure the consistency and stability of negative electrode material quality.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A method for improving and optimizing graphitization process parameters of negative electrode materials, comprising: Based on the material loading of the serial multi-cavity crucible, a historical production data set containing temperature gradient, current and graphitization process data of the negative electrode material was constructed; Based on historical production data sets, the graphitization process status of negative electrode materials at each stage at different temperatures is obtained, and a graphitization prediction model is constructed; The pot body is divided into multiple heating zones, and the real-time temperature data of each zone is obtained through distributed temperature sensors. The uniform temperature value in the crucible is obtained by dynamically controlling the current density. Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization progress in the future time period and formulate temperature control instructions; Compare the real-time graphitization process data obtained by the sensor with the predicted graphitization process data to obtain an error value, and determine whether to execute the formulated instruction based on the size of the error value; If the error value is large, temperature control instructions are formulated in real time based on the current graphitization process data, and the production results are fed back to the prediction model for model optimization.

[0006] Preferably, the process of obtaining the graphitization process of the negative electrode material at each stage at different temperatures based on the historical production data set and constructing the graphitization prediction model specifically includes: Based on historical production data sets, obtain temperature curve change data in each stage and the degree of graphitization of the negative electrode material; Constructing a graphitization degree time series based on the temperature curve change data and the graphitization degree of the negative electrode material, wherein the graphitization degree in each time stamp corresponds to a temperature; Based on the time series of graphitization degree, the variation characteristics are extracted to obtain the graphitization trend of the negative electrode material with temperature change; A graphitization prediction model is constructed based on the change characteristics and graphitization trends.

[0007] Preferably, the step of dividing the pot into a plurality of heating zones, acquiring real-time temperature data of each zone through a distributed temperature sensor, and obtaining a uniform temperature value in the crucible by dynamically controlling the current density specifically includes: The temperature distribution data of each heating area in the crucible is obtained through the distributed temperature sensors in the crucible; Obtain outliers in the temperature data based on the interquartile range, calculate the average temperature in the crucible after removing the outliers, and set it as the target temperature; Based on the outlier temperature data, locate the region in the crucible to which it belongs; Based on the region to which the located outlier temperature belongs, the temperature is adjusted to the target temperature by controlling the current density in the region; Based on the adjusted temperature data, the above steps are repeated until there are no temperature outliers and a uniform value of the temperature in the crucible is obtained.

[0008] Preferably, the step of substituting the acquired temperature data into a graphitization prediction model to obtain the graphitization progress in a future time period and formulate temperature control instructions specifically includes: Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization process and change trend in the future time period; Add timestamps based on inflection points in graphitization trends; Based on the added timestamp, a gradient temperature change strategy is used to formulate temperature control instructions; The gradient temperature change strategy is specifically as follows: The heating process is divided into multiple heating stages based on the number of inflection points. The temperature in the crucible is raised to the specified temperature at different heating rates in different heating stages, and the final temperature is controlled at 3000 .

[0009] Preferably, the real-time graphitization process data acquired based on the sensor is compared with the predicted graphitization process data to obtain an error value, and judging whether to execute the formulated instruction based on the size of the error value specifically includes: The real-time graphitization process of the negative electrode material is obtained through the spectral sensor in the crucible; Aligning the time stamps of the graphitization process obtained in real time with the predicted desertification process obtained through the graphitization prediction model; Obtain the error between the real-time process and the predicted process after timestamp alignment; Based on the size of the error value, if it is within the threshold range, the temperature control instruction formulated based on the gradient temperature change strategy is executed.

[0010] Preferably, if the error value is large, temperature control instructions are formulated in real time according to the current graphitization process data, and the production results are fed back to the prediction model for model optimization, which specifically includes: If the error value exceeds the threshold range, the temperature rise stage is divided based on the inflection point of the current graphitization process; Performing a temperature increase operation based on a gradient temperature change strategy based on the divided temperature increase stages; Based on the temperature change data and graphitization process data of the negative electrode material in the above steps, the graphitization prediction model is retrained to optimize the prediction results and improve the prediction type and accuracy.

[0011] Furthermore, a negative electrode material graphitization process parameter improvement and optimization system is proposed, comprising: Distributed sensors: Real-time distributed sensors are mainly used to obtain temperature data in the crucible and the graphitization process of the negative electrode material; Model building module: the model building module is mainly used to build a graphitization prediction model; Temperature unification module: The temperature unification module is mainly used to unify the temperature of each area in the crucible to facilitate subsequent model operation; Comparison module: The comparison module is mainly used to compare the real-time graphitization process data obtained based on the sensor with the predicted graphitization process data to obtain an error value; Temperature control instruction module: The temperature control instruction module is mainly used to formulate temperature control instructions based on the gradient temperature change strategy to heat the negative electrode material in the crucible; Optimization module: The optimization module is mainly used to use the real-time temperature control strategy under the condition of large error value as a training sample to optimize the graphitization prediction model; Database module: The database module is mainly used to store real-time data and historical data in the crucible, as well as training data and optimization data of the graphitization prediction model; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0012] Compared with the prior art, the advantages of the present invention are: By loading materials based on a series-type multi-cavity crucible and constructing a historical production data set containing temperature gradient, current and graphitization process data, it is possible to accurately analyze the various key parameters in the graphitization process, thereby providing data support for the optimization of the graphitization process. By establishing a graphitization prediction model and combining it with real-time temperature data obtained by distributed temperature sensors, the temperature in the crucible can be dynamically adjusted to achieve more precise temperature control. This method can not only predict future processes in real time during the graphitization process, but also compare the real-time temperature and graphitization state with the predicted data, promptly detect deviations and adjust the control strategy to ensure the accuracy and stability of the temperature control process. If the error value is too large, the system can adjust the temperature control instructions according to the current actual graphitization process to ensure product quality consistency. In addition, by feeding back the production results to the prediction model for optimization, a closed-loop control is formed, and the prediction accuracy of the model and the process optimization level are continuously improved. The present invention also optimizes the common shortcomings of low output of internal series furnaces and high crucible costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of the method proposed in the present invention; Figure 2 A schematic diagram of the prediction model proposed in the present invention; Figure 3 This is a schematic diagram of temperature unification proposed by the present invention; Figure 4 This is a schematic diagram of the process acquisition and temperature control proposed by the present invention; Figure 5 This is a schematic diagram of error judgment proposed by the present invention; Figure 6 This is a schematic diagram of the model optimization proposed by the present invention; Figure 7 This is a diagram of the architecture of the electronic equipment in this solution; Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0014] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0015] A negative electrode material graphitization process parameter improvement and optimization system, comprising: Distributed sensors: Real-time distributed sensors are mainly used to obtain temperature data in the crucible and the graphitization process of the negative electrode material; Model building module: the model building module is mainly used to build a graphitization prediction model; Temperature unification module: The temperature unification module is mainly used to unify the temperature of each area in the crucible to facilitate subsequent model operation; Comparison module: The comparison module is mainly used to compare the real-time graphitization process data obtained based on the sensor with the predicted graphitization process data to obtain an error value; Temperature control instruction module: The temperature control instruction module is mainly used to formulate temperature control instructions based on the gradient temperature change strategy to heat the negative electrode material in the crucible; Optimization module: The optimization module is mainly used to use the real-time temperature control strategy under the condition of large error value as a training sample to optimize the graphitization prediction model; Database module: The database module is mainly used to store real-time data and historical data in the crucible, as well as training data and optimization data of the graphitization prediction model; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0016] See Figure 1 As shown, a method for improving and optimizing the graphitization process parameters of a negative electrode material comprises: Step 1: Load materials into a serial multi-cavity crucible and construct a historical production dataset containing temperature gradient, current, and graphitization process data of the anode material. Step 2: Based on the historical production data set, obtain the graphitization process status of the negative electrode material at each stage at different temperatures and build a graphitization prediction model; Step 3: Divide the pot into multiple heating zones, obtain real-time temperature data of each zone through distributed temperature sensors, and obtain a uniform temperature value in the crucible by dynamically controlling the current density; Step 4: Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization process in the future time period and formulate temperature control instructions; Step 5: Compare the real-time graphitization process data obtained by the sensor with the predicted graphitization process data to obtain an error value, and determine whether to execute the formulated instruction based on the size of the error value; Step 6: If the error value is large, temperature control instructions are formulated in real time based on the current graphitization process data, and the production results are fed back to the prediction model for model optimization.

[0017] In a serial multi-chamber crucible, the material is distributed across multiple chambers, each potentially experiencing different temperatures, resulting in varying temperature gradients. Temperature is a key factor influencing the degree of graphitization of the anode material. Therefore, temperature sensors are installed at various locations within the crucible to capture temperature data within each chamber and calculate the temperature gradient.

[0018] Current data is closely related to the graphitization process because the current can reflect the changes in heating power. By measuring the current of the crucible heating system, the heating efficiency and material response can be indirectly reflected.

[0019] The degree of graphitization is an important indicator that indicates the graphitization process of the negative electrode material. At each time point t, the corresponding degree of graphitization can be measured. , [0,1].

[0020] See Figure 2 As shown in the figure, based on the historical production data set, the graphitization process status of the negative electrode material at each stage at different temperatures is obtained, and the graphitization prediction model is constructed, which specifically includes: Based on historical production data sets, obtain temperature curve change data in each stage and the degree of graphitization of the negative electrode material; Based on the temperature curve change data and the degree of graphitization of the negative electrode material Constructing a time series of graphitization degree , the graphitization degree in each time stamp corresponds to a temperature; Based on the time series of graphitization degree, the variation characteristics are extracted to obtain the graphitization trend of the negative electrode material with temperature change; A graphitization prediction model is constructed based on the change characteristics and graphitization trends.

[0021] Specifically, the temperature change data is associated with the graphitization degree to construct a graphitization degree time series. Set the timestamp For each data point, the graphitization degree S(t) corresponds to the temperature T(t) at each time stamp. The time series is in the form of: There is usually a functional relationship between the degree of graphitization and temperature, which can be described by a linear regression model, as follows: ,After obtaining the relationship between temperature and ,the degree of graphitization, for each time stamp, the corresponding temperature value is substituted ,into the graphitization degree to calculate the degree of graphitization. ,Based on the graphitization degree values calculated at each time stamp, a complete ,time series of the degree of graphitization is formed.

[0022] See Figure 3 As shown, the pot body is divided into multiple heating areas, and the real-time temperature data of each area is obtained through distributed temperature sensors. By dynamically controlling the current density, a uniform temperature value in the crucible is obtained. Specifically, the following steps are performed: The temperature distribution data of each heating area in the crucible is obtained through the distributed temperature sensors in the crucible; Obtain outliers in the temperature data based on the interquartile range, calculate the average temperature in the crucible after removing the outliers, and set it as the target temperature; Based on the outlier temperature data, locate the region in the crucible to which it belongs; Based on the region to which the located outlier temperature belongs, the temperature is adjusted to the target temperature by controlling the current density in the region; Based on the adjusted temperature data, the above steps are repeated until there are no temperature outliers and a uniform value of the temperature in the crucible is obtained.

[0023] Specifically, the temperature change in each heating area has a certain relationship with the heat power. Assuming that the heat conduction in the area is uniform, the temperature change can be described by the heat conduction equation, which is: in, is the specific heat capacity of the ith region, is the mass of the ith region, is the heat loss coefficient, is the actual temperature of the ith region, is the ambient temperature, is the thermal power of the ith region.

[0024] The power output of each zone is adjusted by controlling the current density of each heating zone. The current density is adjusted by the feedback control strategy to reduce the deviation between the target temperature and the actual temperature. The feedback control formula is: in, is the current density, is the proportional gain, is the integral gain, is the differential gain, is the deviation between the target temperature and the actual temperature.

[0025] When the steady state is reached, the temperature of all heating areas will be close to the target temperature. The current density will tend to a stable value, that is, the current in each area will no longer change dramatically. In the process of obtaining outliers in temperature data based on the interquartile range and calculating the average temperature in the crucible after removing the outliers, special attention should be paid to the accurate identification and processing of outliers. The interquartile range method determines the outliers in the data by calculating the first quartile and the third quartile of the data. When a temperature data point exceeds the upper or lower limit, the point is considered an outlier. In actual operation, when controlling the regional current density, it is necessary to consider the spatial distribution differences of the temperature in each region. In the process of adjusting the current density, it is necessary to gradually monitor the temperature changes fed back by each sensor to ensure that the temperature control adjustment can not only quickly eliminate the temperature outliers, but also avoid local temperature fluctuations due to excessive adjustment, and ensure that the temperature in the crucible is finally unified to the target value.

[0026] See Figure 4 As shown, based on the acquired temperature data, it is substituted into the graphitization prediction model to obtain the graphitization process in the future time period and formulate temperature control instructions, including: Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization process and change trend in the future time period; Add timestamps based on inflection points in graphitization trends; Based on the added timestamp, a gradient temperature change strategy is used to formulate temperature control instructions; The gradient temperature change strategy is specifically as follows: The heating process is divided into multiple heating stages based on the number of inflection points. The temperature in the crucible is raised to the specified temperature at different heating rates in different heating stages, and the final temperature is controlled at 3000 .

[0027] Specifically, a local extreme value detection algorithm (such as discrete second-order difference method) is used. ,like The sign changes, It may be a turning point.

[0028] Based on the number of inflection points k identified, the entire heating process is divided into k+1 stages, and each stage corresponds to a heating rate. The time interval between each stage and temperature increment Decide, In the initial stage, the temperature change has little effect on the graphitization reaction, so a slower heating rate is used. In the intermediate stage (the main graphitization reaction stage), the temperature change has a greater impact on the degree of graphitization, so the heating rate needs to be appropriately accelerated. In the final stage, the degree of graphitization tends to be stable. In order to avoid damage to the carbon structure, the heating rate is gradually reduced.

[0029] See Figure 5 As shown, the real-time graphitization process data obtained by the sensor is compared with the predicted graphitization process data to obtain an error value, and the determination of whether to execute the formulated instruction based on the size of the error value specifically includes: The real-time graphitization process of the negative electrode material is obtained through the spectral sensor in the crucible; Aligning the time stamps of the graphitization process obtained in real time with the predicted desertification process obtained through the graphitization prediction model; Obtain the error between the real-time process and the predicted process after timestamp alignment; Based on the size of the error value, if it is within the threshold range, the temperature control instruction formulated based on the gradient temperature change strategy is executed.

[0030] Specifically, the timestamps of real-time data and forecast data are different. Interpolation methods can be used to ensure that they are compared at the same time point. The linear interpolation method can be used as follows: in, 、 The prediction process adjacent time points, The time point for obtaining real-time data.

[0031] Understandably, the gradient temperature change strategy assumes that temperature adjustment occurs gradually. However, in practice, due to the thermal inertia of the material and the response lag of the heating system, temperature control may not achieve the desired effect immediately. The gradient change rate can be dynamically adjusted based on the response time of the temperature control system and the thermal characteristics of the anode material to avoid system instability caused by excessively rapid temperature adjustments. By monitoring the temperature and graphitization progress in real time and regularly correcting the prediction model, the accuracy of the temperature control instructions can be ensured and system lag can be reduced.

[0032] See Figure 6 As shown, if the error value is large, temperature control instructions are formulated in real time based on the current graphitization process data, and the production results are fed back to the prediction model for model optimization, specifically including: If the error value exceeds the threshold range, the temperature rise stage is divided based on the inflection point of the current graphitization process; Performing a temperature increase operation based on a gradient temperature change strategy based on the divided temperature increase stages; Based on the temperature change data and graphitization process data of the negative electrode material in the above steps, the graphitization prediction model is retrained to optimize the prediction results and improve the prediction type and accuracy.

[0033] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 7 The electronic device architecture shown in FIG. Figure 7 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store a method and system for improving and optimizing the graphitization process parameters of a negative electrode material provided in this application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0034] Figure 8 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, a method and system for improving and optimizing the graphitization process parameters of a negative electrode material according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0035] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0036] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for improving and optimizing the graphitization process parameters of negative electrode materials, characterized in that: include: Based on the material loading of the serial multi-cavity crucible, a historical production data set containing temperature gradient, current and graphitization process data of the negative electrode material was constructed; Based on historical production data sets, the graphitization process status of negative electrode materials at each stage at different temperatures is obtained, and a graphitization prediction model is constructed; The pot body is divided into multiple heating zones, and the real-time temperature data of each zone is obtained through distributed temperature sensors. The uniform temperature value in the crucible is obtained by dynamically controlling the current density. Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization progress in the future time period and formulate temperature control instructions; Compare the real-time graphitization process data obtained by the sensor with the predicted graphitization process data to obtain an error value, and determine whether to execute the formulated instruction based on the size of the error value; If the error value is large, temperature control instructions are formulated in real time based on the current graphitization process data, and the production results are fed back to the prediction model for model optimization.

2. The method for improving and optimizing the graphitization process parameters of anode materials according to claim 1, characterized in that: The process of obtaining the graphitization process status of the negative electrode material at each stage at different temperatures based on the historical production data set and constructing the graphitization prediction model specifically includes: Based on historical production data sets, obtain temperature curve change data in each stage and the degree of graphitization of the negative electrode material; Constructing a graphitization degree time series based on the temperature curve change data and the graphitization degree of the negative electrode material, wherein the graphitization degree in each time stamp corresponds to a temperature; Based on the time series of graphitization degree, the variation characteristics are extracted to obtain the graphitization trend of the negative electrode material with temperature change; A graphitization prediction model is constructed based on the change characteristics and graphitization trends.

3. The method for improving and optimizing graphitization process parameters of negative electrode materials according to claim 1, characterized in that: The method of dividing the pot into multiple heating zones, obtaining real-time temperature data of each zone through distributed temperature sensors, and obtaining a uniform temperature value in the crucible by dynamically controlling the current density specifically includes: The temperature distribution data of each heating area in the crucible is obtained through the distributed temperature sensors in the crucible; Obtain outliers in the temperature data based on the interquartile range, calculate the average temperature in the crucible after removing the outliers, and set it as the target temperature; Based on the outlier temperature data, locate the region in the crucible to which it belongs; Based on the region to which the located outlier temperature belongs, the temperature is adjusted to the target temperature by controlling the current density in the region; Based on the adjusted temperature data, the above steps are repeated until there are no temperature outliers and a uniform value of the temperature in the crucible is obtained.

4. The method for improving and optimizing graphitization process parameters of negative electrode materials according to claim 1, characterized in that: Substituting the acquired temperature data into the graphitization prediction model to obtain the graphitization progress in the future time period and formulate temperature control instructions specifically includes: Substitute the acquired temperature data into the graphitization prediction model to obtain the graphitization process and change trend in the future time period; Add timestamps based on inflection points in graphitization trends; Based on the added timestamp, a gradient temperature change strategy is used to formulate temperature control instructions; The gradient temperature change strategy is specifically as follows: The heating process is divided into multiple heating stages based on the number of inflection points. The temperature in the crucible is raised to the specified temperature at different heating rates in different heating stages, and the final temperature is controlled at 3000 .

5. The method for improving and optimizing the graphitization process parameters of anode materials according to claim 1, characterized in that: The real-time graphitization process data acquired based on the sensor is compared with the predicted graphitization process data to obtain an error value, and judging whether to execute the formulated instruction based on the size of the error value specifically includes: The real-time graphitization process of the negative electrode material is obtained through the spectral sensor in the crucible; Aligning the time stamps of the graphitization process obtained in real time with the predicted desertification process obtained through the graphitization prediction model; Obtain the error between the real-time process and the predicted process after timestamp alignment; Based on the size of the error value, if it is within the threshold range, the temperature control instruction formulated based on the gradient temperature change strategy is executed.

6. The method for improving and optimizing the graphitization process parameters of anode materials according to claim 1, characterized in that: If the error value is large, temperature control instructions are formulated in real time according to the current graphitization process data, and the production results are fed back to the prediction model for model optimization, specifically including: If the error value exceeds the threshold range, the temperature rise stage is divided based on the inflection point of the current graphitization process; Performing a temperature increase operation based on a gradient temperature change strategy based on the divided temperature increase stages; Based on the temperature change data and graphitization process data of the negative electrode material in the above steps, the graphitization prediction model is retrained to optimize the prediction results and improve the prediction type and accuracy.

7. A method for improving and optimizing the graphitization process parameters of a negative electrode material is used to implement a system for improving and optimizing the graphitization process parameters of a negative electrode material as described in any one of claims 1 to 6, characterized in that: include: Distributed sensors: Real-time distributed sensors are mainly used to obtain temperature data in the crucible and the graphitization process of the negative electrode material; Model building module: the model building module is mainly used to build a graphitization prediction model; Temperature unification module: The temperature unification module is mainly used to unify the temperature of each area in the crucible to facilitate subsequent model operation; Comparison module: The comparison module is mainly used to compare the real-time graphitization process data obtained based on the sensor with the predicted graphitization process data to obtain an error value; Temperature control instruction module: The temperature control instruction module is mainly used to formulate temperature control instructions based on the gradient temperature change strategy to heat the negative electrode material in the crucible; Optimization module: The optimization module is mainly used to use the real-time temperature control strategy under the condition of large error value as a training sample to optimize the graphitization prediction model; Database module: The database module is mainly used to store real-time data and historical data in the crucible, as well as training data and optimization data of the graphitization prediction model; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

8. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for improving and optimizing the graphitization process parameters of the negative electrode material as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, a method for improving and optimizing the graphitization process parameters of a negative electrode material according to any one of claims 1 to 6 is implemented.

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