Method for monitoring preparation process of carbon negative electrode material of lithium battery of new energy automobile

Abstract

The invention relates to the technical field of data processing, in particular to a new energy automobile lithium battery carbon negative electrode material preparation process monitoring method, which comprises the following steps: acquiring a temperature data sequence in a reaction chamber; performing time sequence decomposition on the temperature data sequence to obtain a seasonal component change curve, and dividing the seasonal component change curve into a plurality of sub-curves; obtaining the similarity between two adjacent sub-curves to obtain a similarity sequence, carrying out adaptive smoothing processing on the similarity sequence to obtain a smooth similarity sequence, and carrying out sub-sequence division on the smooth similarity sequence to obtain a target sub-sequence; analyzing a similarity change factor of the target sub-sequence, and obtaining an adaptive gamma parameter according to the similarity change factor; based on the adaptive gamma parameters, the temperature prediction value in the future time is obtained by using the support vector regression model, and the temperature in the reaction chamber is adjusted according to the temperature prediction value, so that the accuracy of temperature prediction is improved, and the temperature in the reaction chamber is controlled more timely.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for monitoring a preparation process of a carbon negative electrode material for a lithium battery of a new energy vehicle. Background Art

[0002] The carbon negative electrode material of the lithium battery of new energy vehicles is a conductive material used for the negative electrode of lithium electronic batteries. It is mainly composed of graphite materials (such as natural graphite, artificial graphite, hard carbon, soft carbon, etc.). Its main function is to provide a place for lithium ions to be embedded or de-embedded during the charging and discharging process. The preparation process of the carbon negative electrode material of the lithium battery of new energy vehicles generally includes the steps of raw material selection, pretreatment, carbonization, modification, surface treatment and final product packaging. In the preparation process of the carbon negative electrode material, the temperature control of the reaction chamber is the key, which can directly affect the crystallinity of the material, thereby affecting the stability of various physical and other properties of the carbon negative electrode material. Therefore, in the preparation process of the carbon negative electrode material of the lithium battery, when the temperature in the reaction chamber is monitored to be lower than or higher than the set temperature threshold, the temperature in the reaction chamber can be warned and regulated to ensure that the reaction chamber is at the optimal temperature. However, this method ignores the characteristics of the dynamic change of the temperature in the reaction chamber and the delay of temperature monitoring, so that the temperature in the reaction chamber may be under-compensated or over-compensated, resulting in poor temperature stability in the reaction chamber, thereby affecting the efficiency and quality of the preparation of the carbon negative electrode material.

[0003] At present, for temperature control in the preparation process of carbon negative electrode materials for lithium batteries, the support vector regression (SVR) algorithm is first used to predict the temperature in the reaction chamber during the preparation process of carbon negative electrode materials for lithium batteries to obtain the predicted temperature, and then the temperature in the reaction chamber is controlled in real time according to the predicted temperature. However, in the process of temperature prediction using the support vector regression (SVR) algorithm, it is necessary to select a suitable gamma parameter to control the scope of the kernel function in the support vector regression (SVR) algorithm to ensure the accuracy of temperature prediction. Since there may be multiple reaction states and stages in the preparation process of carbon negative electrode materials for lithium batteries, the time series change pattern of the temperature change in the reaction chamber may also be different in each stage. Therefore, it is necessary to adaptively adjust the gamma parameter according to the temperature data collected in real time to ensure the accuracy of the temperature prediction in the reaction chamber by the support vector regression (SVR) algorithm. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles to solve the problem of how to improve the accuracy of predicting the temperature in the reaction chamber using a support vector regression (SVR) algorithm.

[0005] The present invention provides a method for monitoring the preparation process of a carbon negative electrode material for a new energy vehicle lithium battery. The method comprises the following steps: During the preparation of carbon negative electrode materials for lithium batteries, a temperature data sequence is obtained in the reaction chamber; Performing STL time series decomposition on the temperature data sequence to obtain a seasonal component variation curve constructed by the seasonal component of each temperature data in the temperature data sequence, and dividing the seasonal component variation curve into a plurality of sub-curves according to peak points on the seasonal component variation curve; Acquire the similarity between two adjacent sub-curves to obtain a similarity sequence, perform adaptive smoothing on the similarity sequence to obtain a smoothed similarity sequence, divide the smoothed similarity sequence into at least one sub-sequence, and use the last sub-sequence as the sub-sequence corresponding to the last temperature data in the temperature data sequence, recorded as a target sub-sequence; Analyzing a similarity change factor of the target subsequence, and obtaining an adaptive gamma parameter in a support vector regression model according to the similarity change factor and an element difference between the target subsequence and each of the subsequences; Based on the adaptive gamma parameters, the temperature data sequence is predicted using the support vector regression model to obtain a temperature prediction value in the future, and the temperature in the reaction chamber is adjusted in real time according to the temperature prediction value.

[0006] Preferably, obtaining the similarity between two adjacent sub-curves includes: Obtaining the duration corresponding to each of the sub-curves respectively, and obtaining the degree of change of the corresponding sub-curve according to the difference between the maximum value and the minimum value on each of the sub-curves; For any two adjacent sub-curves, the absolute value of the duration difference and the absolute value of the change degree difference between the two adjacent sub-curves are obtained, and the product of the absolute value of the duration difference and the absolute value of the change degree difference is inversely normalized to obtain the similarity between the two adjacent sub-curves.

[0007] Preferably, the step of performing adaptive smoothing on the similarity sequence to obtain a smoothed similarity sequence includes: Performing a first-order difference on the similarity sequence to obtain a corresponding difference sequence, removing the first similarity in the similarity sequence to obtain a target similarity sequence; For any similarity of the target similarity sequence, according to the position number of any similarity in the target similarity sequence, obtain the differential value corresponding to the same position number in the differential sequence, record it as the target differential value, obtain at least one neighborhood differential value of the target differential value in the differential sequence, calculate the absolute value of the difference between the target differential value and each of the neighborhood differential values, obtain the average absolute value of the difference, normalize the average absolute value of the difference, obtain the corresponding normalized value, round up the product of the normalized value and the preset hyperparameter, and obtain the smoothing window size of any similarity; The smoothing window size of each similarity in the target similarity sequence is obtained, and according to the smoothing window size of each similarity in the target similarity sequence, the similarity sequence is subjected to median filtering to obtain a filtered similarity sequence, which is recorded as a smoothed similarity sequence.

[0008] Preferably, dividing the smoothed similarity sequence into at least one subsequence comprises: In the smooth similarity sequence, the similarity corresponding to the first-order derivative being 0 and the second-order derivative being not 0 is obtained as a division point. If the number of the division points is 0, the smooth similarity sequence is taken as a subsequence; if the number of the division points is not 0, the smooth similarity sequence is divided into at least two subsequences according to all the division points.

[0009] Preferably, the analyzing the similarity change factor of the target subsequence includes: The ratio between two adjacent similarities in the target subsequence is calculated to obtain a ratio mean, and the similarity change factor of the target subsequence is obtained according to the absolute value of the difference between the ratio mean and a constant 1.

[0010] Preferably, obtaining the adaptive gamma parameter in the support vector regression model according to the similarity change factor and the element difference between the target subsequence and each of the subsequences includes: According to the number of elements in each of the subsequences, the mean number of elements is calculated, the ratio between the mean number of elements and the number of elements in the target subsequence is calculated, the product between the inverse of the similarity change factor and the ratio is normalized to obtain a corresponding normalized value, and the adaptive gamma parameters in the support vector regression model are obtained according to the product between the normalized value and the preset conversion hyperparameters of the gamma map.

[0011] Preferably, the real-time adjustment of the temperature in the reaction chamber according to the temperature prediction value comprises: The maximum temperature threshold and the minimum temperature threshold of the reaction chamber in the preparation process of the carbon negative electrode material of the lithium battery are obtained. When the temperature prediction value is greater than the maximum temperature threshold, the reaction chamber is subjected to heat dissipation treatment; when the temperature prediction value is less than the minimum temperature threshold, the reaction chamber is subjected to temperature compensation treatment.

[0012] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In the process of preparing the carbon negative electrode material of lithium battery, the present invention obtains the temperature data sequence in the reaction chamber; performs STL time series decomposition on the temperature data sequence to obtain the seasonal component change curve constructed by the seasonal component of each temperature data in the temperature data sequence, and divides the seasonal component change curve into multiple sub-curves according to the peak points on the seasonal component change curve; obtains the similarity between two adjacent sub-curves to obtain a similarity sequence, performs adaptive smoothing processing on the similarity sequence to obtain a smoothed similarity sequence, divides the smoothed similarity sequence into at least one sub-sequence, and takes the last sub-sequence as the sub-sequence corresponding to the last temperature data in the temperature data sequence, which is recorded as a target sub-sequence; analyzes the similarity change factor of the target sub-sequence, and obtains the adaptive gamma parameter in the support vector regression model according to the similarity change factor and the element difference between the target sub-sequence and each sub-sequence; based on the adaptive gamma parameter, uses the support vector regression model to predict the temperature data sequence to obtain the temperature prediction value in the future time, and adjusts the temperature in the reaction chamber in real time according to the temperature prediction value. Among them, when using the SVR support vector regression model to predict the temperature in the reaction chamber during the preparation process of lithium battery carbon negative electrode materials, the seasonal component change curve is obtained by performing STL decomposition on the temperature data sequence on the time series to characterize the periodic change of temperature, and then the seasonal component change curve is divided into multiple sub-curves according to the peak point of the seasonal component change curve, and one sub-curve represents a temperature change mode, and then the similarity between two adjacent sub-curves is calculated to analyze the difference in the change law and pattern between the latest temperature data and the historical temperature data. The greater the difference, the smaller the reference value for subsequent temperature prediction. Therefore, according to the local fluctuation of the similarity sequence corresponding to the temperature data sequence, the adaptive gamma parameter that is more in line with the scenario is obtained, and then the SVR support vector regression model with the adaptive gamma parameter is used to predict the temperature of the reaction chamber, which can improve the prediction accuracy, and at the same time, make the temperature regulation in the reaction chamber according to the prediction results more timely and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0014] Figure 1 It is a method flow chart of a method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0017] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0018] The specific scenario targeted by the present invention is: when using a support vector regression model (SVR) to predict the temperature in a reaction chamber during the preparation process of a carbon negative electrode material for a lithium battery, a suitable gamma parameter is obtained based on the time series variation pattern and regularity of the temperature in the reaction chamber to control the scope of action of the kernel function of the SVR model, thereby improving the accuracy of temperature prediction.

[0019] See also Figure 1 , is a method flow chart of a method for monitoring the preparation process of a carbon negative electrode material for a lithium battery of a new energy vehicle provided in Example 1 of the present invention, such as Figure 1 As shown, the method may include: Step S101, during the preparation process of the carbon negative electrode material for a lithium battery, a temperature data sequence in a reaction chamber is obtained.

[0020] In the preparation process of lithium battery carbon negative electrode materials, the temperature data in the reaction chamber is collected by the temperature sensor arranged in the reaction chamber to form a time-series temperature data sequence. There is no limit on the sampling frequency and the collection time of the temperature data sequence, and the implementer can set it according to the implementation scenario.

[0021] Step S102, performing STL time series decomposition on the temperature data sequence to obtain a seasonal component variation curve constructed by the seasonal component of each temperature data in the temperature data sequence, and dividing the seasonal component variation curve into a plurality of sub-curves according to the peak points on the seasonal component variation curve.

[0022] In the process of preparing carbon negative electrode materials for lithium batteries of new energy vehicles, some chemical reactions and the influence of the preparation operation process are usually involved, which will cause the temperature in the reaction chamber to have certain dynamic changes. Therefore, it is necessary to ensure that the temperature in the reaction chamber during the preparation of carbon negative electrode materials for lithium batteries is in a relatively stable and appropriate state to ensure the quality of processes such as crystallization of carbon negative electrode materials for lithium batteries. Since the temperature in the reaction chamber is in a dynamic change during the monitoring process, and the temperature monitoring and transmission have a certain time continuity, the traditional monitoring method may ignore the change of the temperature in the reaction chamber in the future period of time, which may lead to over-compensation or under-compensation when monitoring and controlling the temperature in the reaction chamber, thereby affecting the accuracy of temperature control in the reaction chamber during the preparation of carbon negative electrode materials.

[0023] Therefore, the prior art uses a support vector regression (SVR) model to predict the temperature of the reaction chamber. However, when the SVR model is used for temperature prediction, the size of the gamma parameter will affect the control range of the kernel function in the SVR model, thereby affecting the prediction accuracy of the SVR model. And due to the dynamic change of the temperature in the reaction chamber, the temperature change pattern and law at different stages may be different. Therefore, the embodiment of the present invention needs to adjust the gamma parameter of the SVR model according to the dynamic change characteristics of the acquired temperature data sequence to improve the accuracy of the temperature prediction in the reaction chamber.

[0024] First, the temperature data sequence is subjected to STL time series decomposition to obtain a seasonal component change curve constructed by the seasonal component of each temperature data in the temperature data sequence. The horizontal axis of the seasonal component change curve is time, and the vertical axis is the seasonal component of each temperature data in the temperature data sequence. Among them, STL time series decomposition is a well-known technology and will not be described in detail here.

[0025] Then, the AMPD algorithm (Amplitude Modulation Peak Detection) algorithm is used to obtain the peak point in the seasonal component change curve, and the peak point is used as the dividing point to divide the seasonal component change curve into multiple sub-curves, which are used to judge the difference between the new temperature cycle and the historical cycle by analyzing the similarity between the sub-curves. Among them, a sub-curve represents the temperature change within a cycle, and the last temperature data in the default temperature data sequence belongs to the latest acquired temperature data, that is, the new temperature data, and the sub-curve corresponding to the new temperature data also represents a new cycle.

[0026] Step S103, obtaining the similarity between two adjacent sub-curves to obtain a similarity sequence, performing adaptive smoothing processing on the similarity sequence to obtain a smoothed similarity sequence, dividing the smoothed similarity sequence into at least one sub-sequence, and taking the last sub-sequence as the sub-sequence corresponding to the last temperature data in the temperature data sequence, recorded as the target sub-sequence.

[0027] After dividing the seasonal component change curve into multiple sub-curves, the similarity between two adjacent sub-curves is obtained to obtain a similarity sequence. The greater the similarity, the more similar the temperature changes between the two sub-curves, and the closer the corresponding temperature change laws and patterns are. The method for obtaining the similarity between two adjacent sub-curves is: Obtaining the duration corresponding to each of the sub-curves respectively, and obtaining the degree of change of the corresponding sub-curve according to the difference between the maximum value and the minimum value on each of the sub-curves; For any two adjacent sub-curves, the absolute value of the duration difference and the absolute value of the change degree difference between the two adjacent sub-curves are obtained, and the product of the absolute value of the duration difference and the absolute value of the change degree difference is inversely normalized to obtain the similarity between the two adjacent sub-curves.

[0028] In one implementation, taking the ith sub-curve and the i-1th sub-curve as an example, the calculation expression of the similarity between the ith sub-curve and the i-1th sub-curve is: in, represents the similarity between the ith subcurve and the i-1th subcurve, exp() represents an exponential function with a natural constant as the base, represents the duration corresponding to the i-th sub-curve, represents the difference between the maximum and minimum values ​​on the i-th sub-curve, Indicates the duration corresponding to the i-1th sub-curve, represents the difference between the maximum and minimum values ​​on the i-1th sub-curve, and || represents the absolute value sign.

[0029] It should be noted that The larger the value of , the greater the difference in duration between the two sub-curves. The larger the difference in period duration between the two sub-curves, the smaller the similarity between the two sub-curves. The smaller the value of is, the closer the variation range between the two sub-curves is, and the greater the similarity between the two sub-curves is.

[0030] Similarly, the similarity between each two adjacent sub-curves can be obtained to obtain a time-series similarity sequence, which is used to characterize the similarity between adjacent periods in the temperature data sequence. When the similarity sequence is relatively stable, that is, when there is no obvious change amplitude and trend, it means that the change law and pattern of the latest temperature data and the historical temperature data are relatively similar, and the historical temperature data has a greater reference value for future temperature prediction. The corresponding gamma parameter should be larger, so as to control the kernel function of the SVR model to have a larger range of action when making predictions, and then use more historical data as a reference to improve the accuracy of future temperature predictions; on the contrary, a smaller gamma parameter needs to be selected to avoid a larger range of kernel function action, which leads to more historical data participating in the prediction of future temperature, thereby ignoring the latest temperature change pattern in the reaction chamber, and then causing the predicted temperature to be too smooth, reducing the accuracy of future temperature predictions.

[0031] Therefore, in the embodiment of the present invention, the adaptive gamma parameters are obtained based on the fluctuation of the similarity sequence. However, before obtaining the adaptive gamma parameters, in order to avoid the influence of the local fluctuation of the similarity sequence, the similarity sequence is first adaptively smoothed to obtain a smoothed similarity sequence, wherein the method of adaptive smoothing is: Performing a first-order difference on the similarity sequence to obtain a corresponding difference sequence, removing the first similarity in the similarity sequence to obtain a target similarity sequence; For any similarity of the target similarity sequence, according to the position number of any similarity in the target similarity sequence, obtain the differential value corresponding to the same position number in the differential sequence, record it as the target differential value, obtain at least one neighborhood differential value of the target differential value in the differential sequence, calculate the absolute value of the difference between the target differential value and each of the neighborhood differential values, obtain the average absolute value of the difference, normalize the average absolute value of the difference, obtain the corresponding normalized value, round up the product of the normalized value and the preset hyperparameter, and obtain the smoothing window size of any similarity; The smoothing window size of each similarity in the target similarity sequence is obtained, and according to the smoothing window size of each similarity in the target similarity sequence, the similarity sequence is subjected to median filtering to obtain a filtered similarity sequence, which is recorded as a smoothed similarity sequence.

[0032] In one embodiment, a first-order difference is performed on the similarity sequence to obtain a difference sequence. Considering that the first similarity in the similarity sequence belongs to the first data, it has little effect on the overall change trend of the similarity sequence, and in order to monitor the change trend and law of the similarity sequence, when the similarity sequence is subjected to median filtering, the first similarity does not need to be smoothed and remains unchanged, and only each similarity in the similarity sequence except the first similarity is adaptively obtained with a smoothing window for median filtering.

[0033] The sequence after removing the first similarity in the similarity sequence is used as the target similarity sequence. It should be noted that the length of the difference sequence is equal to the length of the target similarity sequence, and the jth difference value in the difference sequence corresponds to the adjacent change difference of the jth similarity in the target similarity sequence. Therefore, taking the jth similarity as an example, the jth difference value is obtained in the difference sequence as the difference value corresponding to the jth similarity, and then the jth difference value is centered in the difference sequence, and the 5 difference values ​​before and after it are selected as the neighborhood difference values. According to the difference between the jth difference value and each neighborhood difference value, the calculation expression for obtaining the smoothing window size of the jth similarity is: in, represents the smoothing window size of the j-th similarity, norm() represents the normalization function, Represents the number of neighborhood difference values ​​corresponding to the difference value of the jth similarity in the difference sequence, represents the difference value corresponding to the jth similarity in the difference sequence, represents the yth neighborhood difference value of the difference value corresponding to the jth similarity in the difference sequence, || represents the absolute value sign, and K represents the hyperparameter.

[0034] It should be noted that the value of K is 11, which is not limited here; It is used to characterize the overall difference between the jth difference value and the neighborhood difference value. The larger the overall difference is, the worse the smoothness of the change between the jth similarity in the target similarity sequence and the similarity in the local range before and after it is, and the larger the window of the jth similarity filter smoothing is.

[0035] Similarly, the smoothing window size of each similarity in the target similarity sequence can be obtained, and then the similarity sequence is median filtered according to the smoothing window size of each similarity in the target similarity sequence to obtain a filtered similarity sequence, which is recorded as a smoothed similarity sequence. The median filtering belongs to the prior art and will not be repeated here.

[0036] In order to more intuitively present the changing trend of the new temperature data (that is, the last temperature data) in the temperature data sequence, the smooth similarity sequence is divided into subsequences, and the last subsequence divided is used as the subsequence corresponding to the new temperature data, recorded as the target subsequence. The method for subsequence division of the smooth similarity sequence is: In the smooth similarity sequence, the similarity corresponding to the first-order derivative being 0 and the second-order derivative being not 0 is obtained as a division point. If the number of the division points is 0, the smooth similarity sequence is taken as a subsequence; if the number of the division points is not 0, the smooth similarity sequence is divided into at least two subsequences according to all the division points. The judgment that the first-order derivative is 0 and the second-order derivative is 0 belongs to the prior art and is not described here.

[0037] It should be noted that when there is no division point in the smooth similarity sequence, it means that the fluctuation of the smooth similarity sequence is relatively stable, and the smooth similarity sequence is directly used as the target subsequence.

[0038] At this point, the similarity subsequence corresponding to the new temperature data is obtained, which is also the target subsequence.

[0039] Step S104: Analyze the similarity variation factor of the target subsequence, and obtain the adaptive gamma parameter in the support vector regression model according to the similarity variation factor and the element difference between the target subsequence and each subsequence.

[0040] If the target subsequence shows an upward or downward trend, it means that the change law and pattern of the new temperature data is more different from the change pattern of the historical temperature data, and the dependence of future temperature data on the historical temperature data may be weaker. Therefore, the similarity change factor of the target subsequence is analyzed to detect the change of the new temperature data. The method for obtaining the similarity change factor of the target subsequence is: The ratio between two adjacent similarities in the target subsequence is calculated to obtain a ratio mean, and the similarity change factor of the target subsequence is obtained according to the absolute value of the difference between the ratio mean and a constant 1.

[0041] In one embodiment, the calculation expression of the similarity change factor of the target subsequence is: in, represents the similarity change factor of the target subsequence, represents the number of similarities in the target subsequence, represents the r-th similarity in the target subsequence, represents the r+1th similarity in the target subsequence, 1 represents a constant, and || represents the absolute value sign.

[0042] It should be noted that It is used to characterize the mean ratio between adjacent similarities in the similarity subsequence corresponding to the new temperature data. The less similar the mean ratio is to the constant 1, the more serious the upward or downward trend of the target subsequence may be, and the greater the similarity change factor of the corresponding target subsequence is.

[0043] Furthermore, the adaptive gamma parameter of the SVR model is obtained by combining the similarity change factor of the target subsequence and the proportional relationship of the similarity quantity in each subsequence corresponding to the temperature data sequence, which is used to predict the temperature data in the future. The method for obtaining the adaptive gamma parameter is: According to the number of elements in each of the subsequences, the mean number of elements is calculated, the ratio between the mean number of elements and the number of elements in the target subsequence is calculated, the product between the inverse of the similarity change factor and the ratio is normalized to obtain a corresponding normalized value, and the adaptive gamma parameters in the support vector regression model are obtained according to the product between the normalized value and the preset conversion hyperparameters of the gamma map.

[0044] In one embodiment, the calculation expression of the adaptive gamma parameter is: in, represents the adaptive gamma parameter, norm() represents the normalization function, represents the similarity change factor of the target subsequence, represents the number of subsequences, represents the number of elements in the target subsequence, that is, the number of similarities in the target subsequence. represents the mean number of elements in all subsequences, Represents the preset transformation hyperparameters for the gamma map.

[0045] It should be noted that the value of G is 5, which is not limited here; The smaller the value is, the weaker the dependence of the local change of the new temperature data on the historical temperature data is, and the smaller the value of the corresponding gamma parameter should be; The smaller the value, the faster the local change rate of the new temperature data is relative to the local change rate of the historical temperature data. That is, the shorter the seasonal cycle of the new temperature data exists in the time series, the smaller the value of the gamma parameter should be, and more attention should be paid to the new temperature data during prediction, so that the future temperature data prediction will be less dependent on the historical temperature data.

[0046] So far, the adaptive gamma parameters for predicting future temperature of temperature data series using the SVR model have been obtained.

[0047] Step S105, based on the adaptive gamma parameter, the temperature data sequence is predicted using a support vector regression model to obtain a temperature prediction value in the future, and the temperature in the reaction chamber is adjusted in real time according to the temperature prediction value.

[0048] After determining the gamma parameters of the SVR model, the SVR model can be used to predict the temperature data sequence to obtain the temperature prediction value in the future time, wherein the SVR model belongs to the prior art and will not be described in detail here. Normally, the heat treatment temperature of the carbon negative electrode material of the lithium battery is between 500 degrees and 1000 degrees. Therefore, the maximum temperature threshold of the reaction chamber in the preparation process of the carbon negative electrode material of the lithium battery is set to 1000 degrees and the minimum temperature threshold is 500 degrees. Considering that the temperature change of the reaction chamber is different in timing and each preparation stage, therefore, in the embodiment of the present invention, there is no restriction on the maximum temperature threshold and the minimum temperature threshold, and the implementer can set it according to the implementation scenario.

[0049] Furthermore, the temperature in the reaction chamber is adjusted in real time according to the maximum temperature threshold, the minimum temperature threshold and the temperature prediction value: the maximum temperature threshold and the minimum temperature threshold of the reaction chamber during the preparation process of the lithium battery carbon negative electrode material are obtained, and when the temperature prediction value is greater than the maximum temperature threshold, the reaction chamber is subjected to heat dissipation treatment; when the temperature prediction value is less than the minimum temperature threshold, the reaction chamber is subjected to temperature compensation treatment.

[0050] In one embodiment, when the predicted temperature value is greater than 1000 degrees, the temperature in the reaction chamber is dissipated by a corresponding temperature control device (such as a ventilation and heat dissipation device) to ensure that the temperature in the reaction chamber is below 1000 degrees; when the predicted temperature value is less than 500 degrees, the temperature in the reaction chamber is heated by a temperature control device such as a heating furnace to make the temperature in the reaction chamber higher than 500 degrees. Thus, the temperature in the reaction chamber can be dissipated or compensated to maintain the temperature in the reaction chamber in a balanced and stable state.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles, characterized in that: The method comprises: During the preparation of carbon negative electrode materials for lithium batteries, a temperature data sequence is obtained in the reaction chamber; Performing STL time series decomposition on the temperature data sequence to obtain a seasonal component variation curve constructed by the seasonal component of each temperature data in the temperature data sequence, and dividing the seasonal component variation curve into a plurality of sub-curves according to peak points on the seasonal component variation curve; Acquire the similarity between two adjacent sub-curves to obtain a similarity sequence, perform adaptive smoothing on the similarity sequence to obtain a smoothed similarity sequence, divide the smoothed similarity sequence into at least one sub-sequence, and use the last sub-sequence as the sub-sequence corresponding to the last temperature data in the temperature data sequence, recorded as a target sub-sequence; Analyzing a similarity change factor of the target subsequence, and obtaining an adaptive gamma parameter in a support vector regression model according to the similarity change factor and an element difference between the target subsequence and each of the subsequences; Based on the adaptive gamma parameters, the temperature data sequence is predicted using the support vector regression model to obtain a temperature prediction value in the future, and the temperature in the reaction chamber is adjusted in real time according to the temperature prediction value.

2. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The obtaining the similarity between two adjacent sub-curves includes: Obtaining the duration corresponding to each of the sub-curves respectively, and obtaining the degree of change of the corresponding sub-curve according to the difference between the maximum value and the minimum value on each of the sub-curves; For any two adjacent sub-curves, the absolute value of the duration difference and the absolute value of the change degree difference between the two adjacent sub-curves are obtained, and the product of the absolute value of the duration difference and the absolute value of the change degree difference is inversely normalized to obtain the similarity between the two adjacent sub-curves.

3. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The step of performing adaptive smoothing on the similarity sequence to obtain a smoothed similarity sequence includes: Performing a first-order difference on the similarity sequence to obtain a corresponding difference sequence, removing the first similarity in the similarity sequence to obtain a target similarity sequence; For any similarity of the target similarity sequence, according to the position number of any similarity in the target similarity sequence, obtain the differential value corresponding to the same position number in the differential sequence, record it as the target differential value, obtain at least one neighborhood differential value of the target differential value in the differential sequence, calculate the absolute value of the difference between the target differential value and each of the neighborhood differential values, obtain the average absolute value of the difference, normalize the average absolute value of the difference, obtain the corresponding normalized value, round up the product of the normalized value and the preset hyperparameter, and obtain the smoothing window size of any similarity; The smoothing window size of each similarity in the target similarity sequence is obtained, and according to the smoothing window size of each similarity in the target similarity sequence, the similarity sequence is subjected to median filtering to obtain a filtered similarity sequence, which is recorded as a smoothed similarity sequence.

4. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The dividing the smoothed similarity sequence into at least one subsequence comprises: In the smooth similarity sequence, the similarity corresponding to the first-order derivative being 0 and the second-order derivative being not 0 is obtained as a division point. If the number of the division points is 0, the smooth similarity sequence is taken as a subsequence; if the number of the division points is not 0, the smooth similarity sequence is divided into at least two subsequences according to all the division points.

5. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The analyzing the similarity change factor of the target subsequence includes: The ratio between two adjacent similarities in the target subsequence is calculated to obtain a ratio mean, and the similarity change factor of the target subsequence is obtained according to the absolute value of the difference between the ratio mean and a constant 1.

6. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The step of obtaining an adaptive gamma parameter in a support vector regression model according to the similarity change factor and the element difference between the target subsequence and each of the subsequences includes: According to the number of elements in each of the subsequences, the mean number of elements is calculated, the ratio between the mean number of elements and the number of elements in the target subsequence is calculated, the product between the inverse of the similarity change factor and the ratio is normalized to obtain a corresponding normalized value, and the adaptive gamma parameters in the support vector regression model are obtained according to the product between the normalized value and the preset conversion hyperparameters of the gamma map.

7. The method for monitoring the preparation process of carbon negative electrode materials for lithium batteries of new energy vehicles according to claim 1, characterized in that: The real-time adjustment of the temperature in the reaction chamber according to the temperature prediction value comprises: The maximum temperature threshold and the minimum temperature threshold of the reaction chamber in the preparation process of the carbon negative electrode material of the lithium battery are obtained. When the temperature prediction value is greater than the maximum temperature threshold, the reaction chamber is subjected to heat dissipation treatment; when the temperature prediction value is less than the minimum temperature threshold, the reaction chamber is subjected to temperature compensation treatment.

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