CN118349053A - A lithium carbonate production automation control method and system - Google Patents
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- XGZVUEUWXADBQD-UHFFFAOYSA-L lithium carbonate Chemical compound [Li+].[Li+].[O-]C([O-])=O XGZVUEUWXADBQD-UHFFFAOYSA-L 0.000 title claims abstract description 35
- 229910052808 lithium carbonate Inorganic materials 0.000 title claims abstract description 35
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Abstract
Description
技术领域Technical Field
本发明一般地涉及数据处理技术领域。更具体地，本发明涉及一种碳酸锂生产自动化控制方法及系统。The present invention generally relates to the field of data processing technology. More specifically, the present invention relates to a lithium carbonate production automation control method and system.
背景技术Background technique
在氢化分解法制备碳酸锂的生产过程中，涉及到精细的化学反应条件控制和高纯度要求，对温度管理尤为敏感，为了提升生产效率、保证产品质量以及提高生产过程中的安全性，对生产中的温度自动化控制就尤为重要。而为了提高温度控制的准确性，首先就需要对采集得到的温度数据进行去噪。常用的一种数据去噪的方法是小波阈值降噪，在采用小波阈值降噪时，涉及到小波基和分解层数的问题，若小波基和分解层数选取的不合适，会影响降噪的质量，为此学者们提出了一种新的方法，首先对采集的数据进行EMD（经验模态分解）将数据分解为多个IMF（内涵模态分量）分量，然后对多个IMF分量分别进行小波阈值去噪。例如武汉理工大学能动学院的王思文和郑卫刚发表的名称为《经验模态分解及其在降噪方面的应用》的期刊，公开了一种基于EMD的小波阈值降噪方法，该方法可以解决直接采用小波阈值降噪存在的缺陷，但是在进行IMF分量的分解过程中会出现端点效应，即由于EMD方法基于局部极值点的搜索和插值，而端点处（数据序列的起始点和终止点）的数据点无法获得足够的邻近极值点进行插值，因此导致了端点效应，导致EMD分解出现异常现象，最终影响数据去噪的效果。In the production process of preparing lithium carbonate by hydrogenation decomposition, it involves fine control of chemical reaction conditions and high purity requirements, and is particularly sensitive to temperature management. In order to improve production efficiency, ensure product quality, and improve safety in the production process, automatic temperature control in production is particularly important. In order to improve the accuracy of temperature control, it is first necessary to denoise the collected temperature data. A commonly used method for data denoising is wavelet threshold denoising. When using wavelet threshold denoising, the problem of wavelet basis and decomposition layer number is involved. If the wavelet basis and decomposition layer number are not selected appropriately, the quality of denoising will be affected. For this reason, scholars have proposed a new method. First, the collected data is decomposed into multiple IMF (intrinsic modal component) components by EMD (empirical mode decomposition), and then the multiple IMF components are denoised by wavelet threshold. For example, Wang Siwen and Zheng Weigang from the School of Energy and Power Engineering of Wuhan University of Technology published a journal titled "Empirical Mode Decomposition and Its Application in Noise Reduction", which disclosed a wavelet threshold denoising method based on EMD. This method can solve the defects of directly using wavelet threshold denoising, but endpoint effects will appear in the process of decomposing IMF components. That is, since the EMD method is based on the search and interpolation of local extreme points, the data points at the endpoints (the starting and ending points of the data sequence) cannot obtain enough neighboring extreme points for interpolation, which leads to endpoint effects, resulting in abnormalities in EMD decomposition, which ultimately affects the effect of data denoising.
现有对端点效应的解决方式为舒忠平提出的对称延拓法，该方法通过在原数据端点处向外对称延拓几个周期的极值点的方式以解决端点效应，但是在对氢化分解法制备碳酸锂生产过程中的温度控制中，其温度数据不会呈现出周期变化的方式，因此，采用现有的对称延拓法会存在延拓长度过长或过短的问题；若延拓长度过长，会导致延拓部分有大量噪声，其数据的可信度较低，若延拓长度过短，则可能遗漏信息，延拓长度过长或过短均会影响去噪效果。The existing solution to the endpoint effect is the symmetrical extension method proposed by Shu Zhongping. This method solves the endpoint effect by symmetrically extending several periodic extreme points outward from the endpoint of the original data. However, in the temperature control of the production process of lithium carbonate prepared by the hydrogenation decomposition method, the temperature data does not show a periodic change. Therefore, the use of the existing symmetrical extension method will have the problem of the extension length being too long or too short; if the extension length is too long, it will cause a lot of noise in the extension part, and the credibility of the data is low. If the extension length is too short, information may be missed. Both too long and too short extension lengths will affect the denoising effect.
发明内容Summary of the invention
为解决现有的对称延拓法存在延拓长度过长导致延拓部分有大量噪声以及延拓长度过短导致遗漏信息的技术问题，本发明在如下的多个方面中提供方案。In order to solve the technical problems in the existing symmetric extension method that the extension length is too long, resulting in a large amount of noise in the extension part, and the extension length is too short, resulting in missing information, the present invention provides solutions in the following aspects.
在第一方面中，本发明提供了一种碳酸锂生产自动化控制方法，包括：In a first aspect, the present invention provides a method for automated control of lithium carbonate production, comprising:
采集氢化反应器内的温度，从而获取温度数据序列；collecting the temperature in the hydrogenation reactor to obtain a temperature data sequence;
确定所述温度数据序列的优选程度最高的延拓长度，并将其记为最优延拓长度，所述优选程度与延拓部分数据序列的噪声表现程度呈负相关，与延拓部分的极值点分布和整个所述温度数据序列的极值点分布之间的一致性程度呈正相关；Determine the extension length with the highest preference of the temperature data sequence, and record it as the optimal extension length, wherein the preference is negatively correlated with the noise performance of the extension part of the data sequence, and positively correlated with the consistency between the extreme value point distribution of the extension part and the extreme value point distribution of the entire temperature data sequence;
采用经验模态分解法对所述温度数据序列进行分解，从而获取IMF分量，在分解的过程中依据所述最优延拓长度结合对称延拓法对所述温度数据序列的端点进行延拓，以解决EMD分解过程中的端点效应；The temperature data sequence is decomposed by using the empirical mode decomposition method to obtain the IMF component. During the decomposition process, the endpoints of the temperature data sequence are extended according to the optimal extension length combined with the symmetric extension method to solve the endpoint effect in the EMD decomposition process;
对各个所述IMF分量进行去噪，并对去噪后的IMF分量进行重构，从而得到去噪后的温度数据序列；De-noising each of the IMF components, and reconstructing the de-noised IMF components, thereby obtaining a de-noised temperature data sequence;
从所述去噪后的温度数据序列中获取距当前时刻最近的温度数据，并将其记为当前温度；Obtaining the temperature data closest to the current moment from the denoised temperature data sequence, and recording it as the current temperature;
依据所述当前温度和目标温度之间的差值对氢化反应器内的温度进行PID控制。The temperature in the hydrogenation reactor is PID controlled according to the difference between the current temperature and the target temperature.
其有益效果为：本发明的碳酸锂生产自动化控制方法在对氢化反应器内的温度进行PID控制之前，先对采集的温度数据序列进行去噪，在去噪时采用经验模态分解方法，较于其他去噪算法可以尽量保留信号的物理意义，去除噪声同时不破坏信号特征，保持温度序列中与生产过程相关的温度变化信息且保证去噪后的温度数据序列的精确度，从而提高对氢化反应器内的温度进行控制时的精确度；此外，在对温度数据序列进行经验模态分解时利用对称延拓法解决端点效应，并选取优选程度最高的延拓长度对温度数据序列进行延拓，从而对延拓长度进行自适应调节，能够有效提高算法的稳健性，避免固定长度导致的过短的延拓长度可能遗漏信息及过长则引入噪声的问题，提高分解精度，进而提高对温度数据序列的去噪效果，进一步提高氢化分解法制备碳酸锂的生产过程中氢化反应器内的温度控制的精度和效率。The beneficial effects are as follows: before the temperature in the hydrogenation reactor is PID-controlled in the automated control method for lithium carbonate production of the present invention, the collected temperature data sequence is first denoised, and the empirical mode decomposition method is used for denoising. Compared with other denoising algorithms, the physical meaning of the signal can be retained as much as possible, the noise can be removed without destroying the signal characteristics, the temperature change information related to the production process in the temperature sequence can be maintained, and the accuracy of the denoised temperature data sequence can be guaranteed, thereby improving the accuracy of controlling the temperature in the hydrogenation reactor; in addition, when the temperature data sequence is empirically decomposed, the symmetric extension method is used to solve the endpoint effect, and the extension length with the highest degree of preference is selected to extend the temperature data sequence, so as to adaptively adjust the extension length, which can effectively improve the robustness of the algorithm, avoid the problem that the too short extension length caused by the fixed length may miss information and the too long extension length may introduce noise, improve the decomposition accuracy, and then improve the denoising effect of the temperature data sequence, and further improve the accuracy and efficiency of the temperature control in the hydrogenation reactor in the production process of preparing lithium carbonate by the hydrogenation decomposition method.
在一个实施例中，确定所述最优延拓长度的方法包括：In one embodiment, the method for determining the optimal extension length includes:
设置延拓长度的初始值，并计算对应的优选程度；Set the initial value of the extension length and calculate the corresponding optimization degree;
以一个极值点的距离为步长对延拓长度进行迭代，并计算每次迭代后的延拓长度对应的优选程度；对延拓长度进行迭代是指使延拓长度按照预设步长增加；一个极值点的距离是指所述温度数据序列中本次迭代前的延拓长度对应的数据序列的端点距离下一个极值点的数据长度；Iterating the extension length with the distance of an extreme point as the step length, and calculating the preferred degree corresponding to the extension length after each iteration; iterating the extension length means increasing the extension length according to the preset step length; the distance of an extreme point refers to the data length from the endpoint of the data sequence corresponding to the extension length before this iteration in the temperature data sequence to the next extreme point;
响应于某次迭代对应的优选程度小于上一次迭代对应的优选程度，则停止迭代，并将所述上一次迭代对应的延拓长度作为最优延拓长度。In response to the fact that the preference level corresponding to a certain iteration is less than the preference level corresponding to the previous iteration, the iteration is stopped, and the extension length corresponding to the previous iteration is used as the optimal extension length.
由于延拓部分的极值点分布和整个所述温度数据序列的极值点分布之间的一致性程度和优选程度呈正相关，若以若干个数据点对应的距离为步长对延拓长度进行迭代，可能会出现延拓后的延拓部分的极值点个数不变的情况，进而导致计算出的优选程度变化较小，从而导致找到优选程度最高的延拓长度需花费较长的时间，本发明的方法在确定最优延拓长度时，通过以一个极值点的距离为步长对延拓长度进行迭代，从而保证延拓后延拓部分的极值点分布发生变化，更高效地找到优选程度最高的延拓长度。Since the consistency and preference between the extreme point distribution of the extended part and the extreme point distribution of the entire temperature data sequence are positively correlated, if the extended length is iterated with the distance corresponding to several data points as the step size, the number of extreme points of the extended part after extension may remain unchanged, which in turn causes the calculated preference to change slightly, resulting in a longer time to find the extended length with the highest preference. When determining the optimal extended length, the method of the present invention iterates the extended length with the distance of an extreme point as the step size, thereby ensuring that the extreme point distribution of the extended part after extension changes, and more efficiently finding the extended length with the highest preference.
在一个实施例中，所述一致性程度依据延拓部分数据序列中极值点的出现频率与所述温度数据序列中极值点的出现频率的差值进行计算，且与所述差值的绝对值呈负相关，所述延拓部分数据序列的噪声表现程度依据延拓部分数据序列中各个温度数据点的噪声表现程度的均值计算，且与所述均值呈负相关。In one embodiment, the consistency level is calculated based on the difference between the frequency of occurrence of extreme points in the extended data sequence and the frequency of occurrence of extreme points in the temperature data sequence, and is negatively correlated with the absolute value of the difference. The noise performance level of the extended data sequence is calculated based on the average of the noise performance levels of each temperature data point in the extended data sequence, and is negatively correlated with the average.
延拓部分数据序列的极值点的出现频率与所述温度数据序列中极值点的出现频率的差值越小，两者的极值点出现频率越一致，表明两者的极值点分布的一致性程度越高，因此，依据延拓部分数据序列的极值点的出现频率与所述温度数据序列中极值点的出现频率的差值可较为准确地计算出延拓部分的极值点分布和所述温度数据序列的极值点分布之间的一致性程度；延拓部分数据序列中各个数据点的噪声表现程度的均值越大，表明整个延拓部分数据序列的噪声表现程度越大，因此，依据延拓部分数据序列中各个数据点的噪声表现程度的均值可较为准确地计算出延拓部分数据序列的噪声表现程度。The smaller the difference between the frequency of occurrence of the extreme points in the extended data sequence and the frequency of occurrence of the extreme points in the temperature data sequence, the more consistent the frequency of occurrence of the extreme points of the two, indicating that the consistency of the distribution of the extreme points of the two is higher. Therefore, based on the difference between the frequency of occurrence of the extreme points in the extended data sequence and the frequency of occurrence of the extreme points in the temperature data sequence, the consistency between the distribution of the extreme points in the extended part and the distribution of the extreme points in the temperature data sequence can be calculated more accurately; the larger the mean value of the noise performance degree of each data point in the extended data sequence is, the greater the noise performance degree of the entire extended data sequence is. Therefore, based on the mean value of the noise performance degree of each data point in the extended data sequence, the noise performance degree of the extended data sequence can be calculated more accurately.
在一个实施例中，所述优选程度的计算表达式为：In one embodiment, the calculation expression of the preference degree is:
； ;
式中，表示延拓部分数据序列的优选程度，表示所述温度数据序列中极值点的出现频率，表示所述延拓部分数据序列中极值点的出现频率，为所述延拓部分数据序列中极值点出现频率与所述温度数据序列中极值点的出现频率的差值，表示所述一致性程度；表示所述延拓部分数据序列中各个数据点的噪声表现程度的均值；B为超参数。In the formula, Indicates the preference for extending part of the data sequence, represents the frequency of occurrence of extreme points in the temperature data sequence, represents the frequency of occurrence of extreme points in the extended data sequence, is the difference between the frequency of occurrence of extreme points in the extended data sequence and the frequency of occurrence of extreme points in the temperature data sequence, Indicates the degree of said consistency; It represents the mean value of the noise performance of each data point in the extended data sequence; B is a hyperparameter.
在一个实施例中，所述数据序列中极值点的出现频率可依据相邻极值点对应的时间差，以及相邻极值点的噪声表现程度之和进行计算，且与所述时间差以及所述噪声表现程度之和均呈负相关；所述相邻极值点对应的时间差是指相邻的两个极值点对应的采集时刻之间的差值。In one embodiment, the frequency of occurrence of extreme points in the data sequence can be calculated based on the time difference corresponding to adjacent extreme points and the sum of the noise performance levels of adjacent extreme points, and is negatively correlated with both the time difference and the sum of the noise performance levels; the time difference corresponding to adjacent extreme points refers to the difference between the collection times corresponding to two adjacent extreme points.
在一个实施例中，数据序列中极值点的出现频率计算表达式为：In one embodiment, the frequency calculation expression of extreme value points in the data sequence is:
； ;
式中，表示数据序列中极值点的出现频率，N表示数据序列中的极值点个数，、分别表示数据序列中第j个极值点和第j+1个极值点对应的时间，、分别表示数据序列中第j个极值点对应的温度数据点的噪声表现程度和第j+1个极值点对应的温度数据点的噪声表现程度。In the formula, represents the frequency of occurrence of extreme points in the data sequence, N represents the number of extreme points in the data sequence, , They represent the time corresponding to the jth extreme point and the j+1th extreme point in the data sequence respectively. , They respectively represent the noise performance degree of the temperature data point corresponding to the j-th extreme point in the data sequence and the noise performance degree of the temperature data point corresponding to the j+1-th extreme point.
相邻数据点对应的时间差越大，表明出现一个极值点后出现下一个极值点所花的时间越长，极值点出现频率越低；极值点的噪声表现程度越大，说明该极值点为噪声的可能性越大，即该极值点为假极值点的可能性越大，因此，相邻极值点的噪声表现程度之和越大，对应的极值点出现频率越低。故此，采用本发明的数据序列中极值点的出现频率计算表达式可准确高效地计算出极值点的出现频率。The larger the time difference corresponding to adjacent data points, the longer it takes for the next extreme point to appear after an extreme point appears, and the lower the frequency of extreme point appearance; the greater the noise performance of the extreme point, the greater the possibility that the extreme point is noise, that is, the greater the possibility that the extreme point is a false extreme point, therefore, the greater the sum of the noise performance of adjacent extreme points, the lower the frequency of the corresponding extreme point appearance. Therefore, the frequency calculation expression of extreme point appearance in the data sequence of the present invention can accurately and efficiently calculate the frequency of extreme point appearance.
在一个实施例中，所述温度数据点的噪声表现程度获取方法包括：In one embodiment, the method for obtaining the noise performance degree of the temperature data point includes:
依据所述温度数据序列获取该温度数据点的邻域范围内的温度数据序列，并将其记为邻域温度数据序列；Acquire a temperature data sequence within the neighborhood of the temperature data point according to the temperature data sequence, and record it as a neighborhood temperature data sequence;
获取所述邻域温度数据序列对应的时间段内各个温度采集时刻的氢化反应器内的压力，从而形成邻域压力数据序列；Acquire the pressure in the hydrogenation reactor at each temperature collection moment within the time period corresponding to the neighborhood temperature data sequence, thereby forming a neighborhood pressure data sequence;
依据所述邻域温度数据序列与所述邻域压力数据序列之间的皮尔逊相关系数和该温度数据点的突变程度计算该温度数据点的噪声表现程度，且该温度数据点的噪声表现程度与所述皮尔逊相关系数呈负相关，与对应的突变程度呈正相关；所述突变程度用于表征该温度数据点的波动幅度。The noise performance degree of the temperature data point is calculated based on the Pearson correlation coefficient between the neighborhood temperature data sequence and the neighborhood pressure data sequence and the mutation degree of the temperature data point, and the noise performance degree of the temperature data point is negatively correlated with the Pearson correlation coefficient and positively correlated with the corresponding mutation degree; the mutation degree is used to characterize the fluctuation amplitude of the temperature data point.
在一个实施例中，所述温度数据点的噪声表现程度计算表达式为：In one embodiment, the noise performance degree calculation expression of the temperature data point is:
； ;
式中，表示第i个温度数据点的噪声表现程度，exp()为指数函数；为第i个温度数据点处的线性取值，表示第i个温度数据点的实际数值，表示第i个温度数据点的突变程度；表示第i个温度数据点的邻域温度数据序列，为第i个温度数据点的邻域压力数据序列，表示邻域温度数据序列与邻域压力数据序列之间的皮尔逊相关系数。In the formula, Indicates the noise performance of the i-th temperature data point, exp() is an exponential function; is the linear value at the i-th temperature data point, represents the actual value of the ith temperature data point, Indicates the degree of mutation of the i-th temperature data point; represents the neighborhood temperature data sequence of the i-th temperature data point, is the neighborhood pressure data sequence of the ith temperature data point, Represents the Pearson correlation coefficient between the neighborhood temperature data series and the neighborhood pressure data series.
温度传感器的采集得到的温度数据中通常会混有噪声，噪声点通常表现为突变点，因此可利用温度数据点的突变程度计算得到其噪声表现程度。但是在氢化分解过程中会涉及吸热反应和放热反应（即Li2CO3+CO2+H2O→2LiHCO3为吸热反应，发生这种反应时温度下降，同时由化学方式，反应过程中吸收了二氧化碳，导致容器内压力降低，而2LiHCO3→Li2CO3↓+CO2↑+H2O为放热反应，发生这种反应时温度上升同时放出气体导致压力上升），这样的吸热或放热反应会导致温度数据中出现突变点，但这种突变点为正常的数据变化，因此需要利用温度数据和压力数据的皮尔逊相关系数（即线性相关程度）对噪声表现程度进行修正，皮尔逊相关系数的取值范围为[-1，+1]，相皮尔逊关系数的值越大，表明温度数据的突变越有可能是压力数据的突变导致的，温度数据的噪声表现程度越小，相反，相关系数的值越小，表明温度数据的突变越有可能是温度数据的噪声引起的，温度数据的噪声表现程度越大，因此，皮尔逊相关系数的值越大，修正后的温度数据的噪声表现程度越小，皮尔逊相关系数越小，修正后的温度数据的噪声表现程度越大。故此，采用本实施例的温度数据点的噪声表现程度计算方法可较为准确且高效地计算出温度数据点的噪声表现程度。The temperature data collected by the temperature sensor is usually mixed with noise, and the noise point is usually manifested as a mutation point. Therefore, the degree of noise performance can be calculated using the mutation degree of the temperature data point. However, the hydrogenation decomposition process involves endothermic reactions and exothermic reactions (i.e. Li 2 CO3+CO 2 +H 2 O→2LiHCO 3 is an endothermic reaction. When this reaction occurs, the temperature drops. At the same time, carbon dioxide is absorbed by the reaction process chemically, resulting in a decrease in the pressure in the container, and 2LiHCO 3 →Li 2 CO 3 ↓+CO 2 ↑+H2O is an exothermic reaction. When this reaction occurs, the temperature rises and gas is released, causing the pressure to rise). Such endothermic or exothermic reactions will cause mutation points in the temperature data, but this mutation point is a normal data change. Therefore, it is necessary to use the Pearson correlation coefficient (i.e., the degree of linear correlation) of the temperature data and the pressure data to correct the noise performance. The value range of the Pearson correlation coefficient is [-1, +1]. The larger the value of the Pearson correlation coefficient, the more likely the mutation of the temperature data is caused by the mutation of the pressure data, and the smaller the noise performance of the temperature data. On the contrary, the smaller the value of the correlation coefficient, the more likely the mutation of the temperature data is caused by the noise of the temperature data, and the greater the noise performance of the temperature data. Therefore, the larger the value of the Pearson correlation coefficient, the smaller the noise performance of the corrected temperature data, and the smaller the Pearson correlation coefficient, the greater the noise performance of the corrected temperature data. Therefore, the noise performance calculation method of the temperature data point of this embodiment can calculate the noise performance of the temperature data point more accurately and efficiently.
在一个实施例中，所述氢化反应器内的温度的采集频率为50HZ。In one embodiment, the sampling frequency of the temperature in the hydrogenation reactor is 50 Hz.
在第二方面中，本发明提供了一种碳酸锂生产自动化控制系统，包括处理器和存储器，所述存储器存储有计算机程序指令，当所述计算机程序指令被所述处理器执行时实现本发明的碳酸锂生产自动化控制方法。In a second aspect, the present invention provides an automated control system for lithium carbonate production, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the automated control method for lithium carbonate production of the present invention is implemented.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
通过参考附图阅读下文的详细描述，本发明示例性实施方式的上述以及其他目的、特征和优点将变得易于理解。在附图中，以示例性而非限制性的方式示出了本发明的若干实施方式，并且相同或对应的标号表示相同或对应的部分，其中：By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:
图1是示意性示出本发明的实施例的碳酸锂生产自动化控制方法流程图；FIG1 is a flow chart schematically showing an automated control method for lithium carbonate production according to an embodiment of the present invention;
图2是示意性示出本发明的实施例的确定所述最优延拓长度的方法流程图；FIG2 is a flow chart schematically illustrating a method for determining the optimal extension length according to an embodiment of the present invention;
图3是示意性示出本发明的实施例的温度数据序列及延拓部分示意图；FIG3 is a schematic diagram schematically showing a temperature data sequence and an extended portion of an embodiment of the present invention;
图4是示意性示出本发明的实施例的温度数据点的噪声表现程度计算方法流程图；FIG4 is a flow chart schematically showing a method for calculating the noise performance degree of a temperature data point according to an embodiment of the present invention;
图5是示意性示出本发明的实施例的温度数据点的线性取值获取方法示意图；FIG5 is a schematic diagram schematically illustrating a method for obtaining linear values of temperature data points according to an embodiment of the present invention;
图6是示意性示出本发明的实施例的碳酸锂生产自动化控制系统结构示意图。FIG6 is a schematic diagram schematically showing the structure of an automated control system for lithium carbonate production according to an embodiment of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
下面结合附图来详细描述本发明的具体实施方式。The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
碳酸锂生产自动化控制方法实施例：Lithium carbonate production automation control method embodiment:
如图1所示，本发明的碳酸锂生产自动化控制方法，包括：As shown in FIG1 , the lithium carbonate production automation control method of the present invention comprises:
S101、采集氢化反应器内的温度，从而获取温度数据序列。S101. Collect the temperature in the hydrogenation reactor to obtain a temperature data sequence.
可在氢化反应器内设置温度传感器，从而实现对氢化反应器内的温度进行采集。采集频率可以为50HZ或其它合适的频率。A temperature sensor may be provided in the hydrogenation reactor to collect the temperature in the hydrogenation reactor. The collection frequency may be 50 Hz or other suitable frequencies.
S102、获取最优延拓长度，具体为：确定所述温度数据序列的优选程度最高的延拓长度，并将其记为最优延拓长度，所述优选程度与延拓部分数据序列的噪声表现程度呈负相关，与延拓部分的极值点分布和整个所述温度数据序列的极值点分布之间的一致性程度呈正相关。S102, obtaining the optimal extension length, specifically: determining the extension length with the highest degree of preference for the temperature data sequence, and recording it as the optimal extension length, wherein the degree of preference is negatively correlated with the degree of noise performance of the extended part of the data sequence, and is positively correlated with the degree of consistency between the extreme value point distribution of the extended part and the extreme value point distribution of the entire temperature data sequence.
在对温度数据进行去噪时，通过采用经验模态分解将其分解为多个内涵模态分量，分别对各个内涵模态分量进行去噪，为了解决对温度数据序列进行经验模态分解时产生的端点效应问题，需对所述温度数据序列进行对称延拓得到延拓部分，延拓长度是指延拓部分所占的长度。When denoising the temperature data, empirical mode decomposition is used to decompose it into multiple intrinsic mode components, and each intrinsic mode component is denoised separately. In order to solve the endpoint effect problem caused by empirical mode decomposition of the temperature data sequence, the temperature data sequence needs to be symmetrically extended to obtain the extended part, and the extended length refers to the length occupied by the extended part.
在氢化分解法制备碳酸锂生产过程中涉及许多化学反应，其吸热放热反应会导致氢化反应器中的数据发生变化，并且不同时刻数据变化特征必然不同，这就会导致在利用对称延拓法解决EMD分解中的端口效应时，其延拓长度难以确定。具体地，较长的延拓长度有助于将数据中的细节信息更好的保留，但在此过程中也可能引入更多的噪声，过多的噪声也会导致计算误差，因此需要使延拓过程中延拓数据段内的噪声水平尽可能低，从而保证延拓部分数据具有较高的可信度。There are many chemical reactions involved in the production process of lithium carbonate prepared by hydrogenation decomposition. The endothermic and exothermic reactions will cause the data in the hydrogenation reactor to change, and the characteristics of data changes at different times must be different, which will make it difficult to determine the extension length when using the symmetric extension method to solve the port effect in EMD decomposition. Specifically, a longer extension length helps to better retain the detailed information in the data, but it may also introduce more noise in the process. Excessive noise will also lead to calculation errors. Therefore, it is necessary to make the noise level in the extension data segment as low as possible during the extension process to ensure that the extended part of the data has a high degree of credibility.
通过使延拓部分的极值点分布和整个所述温度数据序列的极值点分布之间的一致性程度尽可能高，可以保证分解出的固有模态函数更好的反映局部特征，使得对称延拓法更好地解决EMD算法的端点效应；提高EMD分解的精度，提升去噪效果。通过计算最优延拓长度，以便在后续对温度数据序列进行对称延拓后，延拓部分数据具有较高的可信度且保障分解出的固有模态函数更好的反映局部特征。By making the degree of consistency between the extreme point distribution of the extended part and the extreme point distribution of the entire temperature data sequence as high as possible, it can be ensured that the decomposed intrinsic mode function better reflects the local characteristics, so that the symmetric extension method can better solve the endpoint effect of the EMD algorithm; improve the accuracy of EMD decomposition and enhance the denoising effect. By calculating the optimal extension length, after the temperature data sequence is symmetrically extended in the future, the extended part data has a higher credibility and ensures that the decomposed intrinsic mode function better reflects the local characteristics.
延拓部分的极值点分布和整个所述温度数据序列的极值点分布的一致性程度的确定方法有多种，例如依据各个极值点的位置进行确定，可通过分析延拓部分的所有极值点中和所述温度数据序列的极值点关于延拓中心对称的极值点所占的比例进行确定，所占的比例越大，对应的一致性程度越高。There are many ways to determine the degree of consistency between the extreme point distribution of the extended part and the extreme point distribution of the entire temperature data sequence. For example, it can be determined based on the position of each extreme point. It can be determined by analyzing the proportion of all extreme points of the extended part and the extreme points of the temperature data sequence that are symmetrical about the extension center. The larger the proportion, the higher the corresponding degree of consistency.
也可依据延拓部分的极值点的纵坐标与温度数据序列的极值点的纵坐标之间的偏差进行确定，例如可分别计算延拓部分的极值点的纵坐标的均值和温度数据序列的极值点的纵坐标均值，通过两个均值之间的偏差确定一致性程度。It can also be determined based on the deviation between the ordinates of the extreme points of the extended part and the extreme points of the temperature data sequence. For example, the mean of the ordinates of the extreme points of the extended part and the mean of the ordinates of the extreme points of the temperature data sequence can be calculated respectively, and the degree of consistency can be determined by the deviation between the two means.
也可以依据延拓部分的极值点出现的频率和所述温度数据序列的极值点出现的频率之间的偏差进行确定，偏差越小对应的一致性程度越高。It can also be determined based on the deviation between the frequency of occurrence of the extreme points of the extended part and the frequency of occurrence of the extreme points of the temperature data sequence. The smaller the deviation, the higher the corresponding consistency.
S103、对所述温度数据序列进行经验模态分解，具体为：采用经验模态分解法对所述温度数据序列进行分解，从而获取IMF分量，在分解的过程中依据所述最优延拓长度结合对称延拓法对所述温度数据序列的端点进行延拓，以解决EMD分解过程中的端点效应。S103, performing empirical mode decomposition on the temperature data sequence, specifically: using empirical mode decomposition to decompose the temperature data sequence to obtain IMF components, and in the decomposition process, extending the endpoints of the temperature data sequence according to the optimal extension length combined with the symmetric extension method to solve the endpoint effect in the EMD decomposition process.
S104、对各个所述IMF分量进行去噪，并对去噪后的IMF分量进行重构，从而得到去噪后的温度数据序列。S104, denoising each of the IMF components, and reconstructing the denoised IMF components, so as to obtain a denoised temperature data sequence.
在对IMF分量进行去噪时可采用小波阈值去噪法、阈值去噪或其它合适的方法，优选地，本实施例中采用小波阈值去噪法。在进行重构时可采用最小二乘法或正则化方法。When denoising the IMF component, wavelet threshold denoising, threshold denoising or other suitable methods may be used. Preferably, the wavelet threshold denoising method is used in this embodiment. When reconstructing, the least square method or regularization method may be used.
S105、从所述去噪后的温度数据序列中获取距当前时刻最近的温度数据，并将其记为当前温度。S105 , obtaining the temperature data closest to the current moment from the denoised temperature data sequence, and recording it as the current temperature.
S106、依据所述当前温度和目标温度之间的差值对氢化反应器内的温度进行PID控制。S106, performing PID control on the temperature in the hydrogenation reactor according to the difference between the current temperature and the target temperature.
本发明的碳酸锂生产自动化控制方法在对氢化反应器内的温度进行PID控制之前，先对采集的温度数据序列进行去噪，在去噪时采用经验模态分解方法，较于其他去噪算法可以尽量保留信号的物理意义，去除噪声同时不破坏信号特征，保持温度序列中与生产过程相关的温度变化信息且保证去噪后的温度数据序列的精确度，从而提高对氢化反应器内的温度进行控制时的精确度；此外，在对温度数据序列进行经验模态分解时利用对称延拓法解决端点效应，并选取优选程度最高的延拓长度对温度数据序列进行延拓，从而对延拓长度进行自适应调节，能够有效提高算法的稳健性，避免固定长度导致的过短的延拓长度可能遗漏信息及过长则引入噪声的问题，提高分解精度，进而提高对温度数据序列的去噪效果，进一步提高氢化分解法制备碳酸锂的生产过程中氢化反应器内的温度进行控制的精度和效率。The lithium carbonate production automation control method of the present invention first denoises the collected temperature data sequence before performing PID control on the temperature in the hydrogenation reactor. The empirical mode decomposition method is used during denoising. Compared with other denoising algorithms, the physical meaning of the signal can be retained as much as possible, the noise is removed without destroying the signal characteristics, the temperature change information related to the production process in the temperature sequence is maintained, and the accuracy of the denoised temperature data sequence is guaranteed, thereby improving the accuracy when the temperature in the hydrogenation reactor is controlled; in addition, when the temperature data sequence is subjected to empirical mode decomposition, the symmetric extension method is used to solve the endpoint effect, and the extension length with the highest degree of preference is selected to extend the temperature data sequence, so as to adaptively adjust the extension length, which can effectively improve the robustness of the algorithm, avoid the problem that the too short extension length caused by the fixed length may omit information and the too long extension length may introduce noise, improve the decomposition accuracy, and then improve the denoising effect of the temperature data sequence, and further improve the accuracy and efficiency of controlling the temperature in the hydrogenation reactor in the production process of preparing lithium carbonate by the hydrogenation decomposition method.
如图2所示，在一个实施例中，确定所述最优延拓长度的方法包括：As shown in FIG. 2 , in one embodiment, the method for determining the optimal extension length includes:
S201、设置延拓长度的初始值，并计算对应的优选程度。S201. Set an initial value of the extension length and calculate the corresponding preference degree.
S202、对延拓长度进行迭代，并计算每次迭代后的延拓长度对应的优选程度，具体为：以一个极值点的距离为步长对延拓长度进行迭代，并计算每次迭代后的延拓长度对应的优选程度；对延拓长度进行迭代是指使延拓长度按照预设步长增加；一个极值点的距离是指所述温度数据序列中本次迭代前的延拓长度对应的数据序列的端点距离下一个极值点的数据长度。S202, iterating the extension length, and calculating the degree of preference corresponding to the extension length after each iteration, specifically: iterating the extension length with the distance of an extreme point as a step length, and calculating the degree of preference corresponding to the extension length after each iteration; iterating the extension length means increasing the extension length according to a preset step length; the distance of an extreme point refers to the data length from the endpoint of the data sequence corresponding to the extension length before this iteration in the temperature data sequence to the next extreme point.
如图3所示，假设采集的温度数据序列为从数据点D到数据点E的数据序列，本次迭代前的延拓部分为从数据点c到数据点b的数据序列，q其中，数据点D为延拓中心点，数据点b和数据点B关于数据点D对称，数据点c和数据点C也关于数据点D对称，所述温度数据序列中本次迭代前的延拓长度对应的数据序列为数据点D到数据点C的数据序列，其右端点为数据点C，从数据点C往右搜索，下一个极值点为数据点A，则从数据点C到数据点A的数据点个数即为一个极值点的距离。As shown in Figure 3, assume that the collected temperature data sequence is a data sequence from data point D to data point E, and the extension part before this iteration is a data sequence from data point c to data point b, where data point D is the extension center point, data point b and data point B are symmetrical about data point D, and data point c and data point C are also symmetrical about data point D. The data sequence corresponding to the extension length before this iteration in the temperature data sequence is a data sequence from data point D to data point C, and its right endpoint is data point C. Searching from data point C to the right, the next extreme point is data point A, and the number of data points from data point C to data point A is the distance of an extreme point.
S203、响应于某次迭代对应的优选程度小于上一次迭代对应的优选程度，则停止迭代，并将所述上一次迭代对应的延拓长度作为最优延拓长度。S203: In response to the fact that the degree of preference corresponding to a certain iteration is less than the degree of preference corresponding to the previous iteration, the iteration is stopped, and the extension length corresponding to the previous iteration is used as the optimal extension length.
由于延拓部分的极值点分布和整个所述温度数据序列的极值点分布之间的一致性程度和优选程度呈正相关，若以若干个数据点对应的距离为步长对延拓长度进行迭代，可能会出现延拓后的延拓部分的极值点个数不变的情况，进而导致计算出的优选程度变化较小，从而导致找到优选程度最高的延拓长度需花费较长的时间，本实施例中的方法在确定最优延拓长度时，通过以一个极值点的距离为步长对延拓长度进行迭代，从而保证延拓后延拓部分的极值点分布发生变化，更高效地找到优选程度最高的延拓长度。Since the consistency and preference between the extreme point distribution of the extended part and the extreme point distribution of the entire temperature data sequence are positively correlated, if the extended length is iterated with the distance corresponding to several data points as the step size, the number of extreme points of the extended part after extension may remain unchanged, which in turn causes the calculated preference to change slightly, resulting in a longer time to find the extended length with the highest preference. When determining the optimal extended length, the method in this embodiment iterates the extended length with the distance of an extreme point as the step size, thereby ensuring that the extreme point distribution of the extended part after extension changes, and more efficiently finding the extended length with the highest preference.
以上实施例中提到，所述优选程度与延拓部分数据序列的噪声表现程度呈负相关，与延拓部分的极值点分布和所述温度数据序列的极值点分布之间的一致性程度呈正相关，在一个实施例中，所述一致性程度依据延拓部分数据序列的极值点的出现频率与所述温度数据序列中极值点的出现频率的差值进行计算，且与所述差值的绝对值呈负相关，所述延拓部分数据序列的噪声表现程度依据延拓部分数据序列中各个数据点的噪声表现程度的均值计算，且与所述均值呈负相关。It is mentioned in the above embodiments that the degree of preference is negatively correlated with the noise performance degree of the extended part data sequence, and is positively correlated with the consistency degree between the extreme point distribution of the extended part and the extreme point distribution of the temperature data sequence. In one embodiment, the consistency degree is calculated based on the difference between the occurrence frequency of the extreme points in the extended part data sequence and the occurrence frequency of the extreme points in the temperature data sequence, and is negatively correlated with the absolute value of the difference. The noise performance degree of the extended part data sequence is calculated based on the mean of the noise performance degrees of each data point in the extended part data sequence, and is negatively correlated with the mean.
延拓部分数据序列的极值点的出现频率与所述温度数据序列中极值点的出现频率的差值越小，两者的极值点出现频率越一致，表明两者的极值点分布的一致性程度越高，因此，依据延拓部分数据序列的极值点的出现频率与所述温度数据序列中极值点的出现频率的差值可较为准确地计算出延拓部分的极值点分布和所述温度数据序列的极值点分布之间的一致性程度；延拓部分数据序列中各个数据点的噪声表现程度的均值越大，表明整个延拓部分数据序列的噪声表现程度越大，因此，依据延拓部分数据序列中各个数据点的噪声表现程度的均值可较为准确地计算出延拓部分数据序列的噪声表现程度。The smaller the difference between the frequency of occurrence of the extreme points in the extended data sequence and the frequency of occurrence of the extreme points in the temperature data sequence, the more consistent the frequency of occurrence of the extreme points of the two, indicating that the consistency of the distribution of the extreme points of the two is higher. Therefore, based on the difference between the frequency of occurrence of the extreme points in the extended data sequence and the frequency of occurrence of the extreme points in the temperature data sequence, the consistency between the distribution of the extreme points in the extended part and the distribution of the extreme points in the temperature data sequence can be calculated more accurately; the larger the mean value of the noise performance degree of each data point in the extended data sequence is, the greater the noise performance degree of the entire extended data sequence is. Therefore, based on the mean value of the noise performance degree of each data point in the extended data sequence, the noise performance degree of the extended data sequence can be calculated more accurately.
由以上实施例可知，所述优选程度与延拓部分数据序列中各个数据点的噪声表现程度的均值呈负相关，与延拓部分的极值点的出现频率与所述温度数据序列中极值点的出现频率两者之间的差值呈负相关，在一个实施例中，所述优选程度的计算表达式为：It can be seen from the above embodiments that the preference degree is negatively correlated with the mean value of the noise performance degree of each data point in the extended part data sequence, and is negatively correlated with the difference between the occurrence frequency of the extreme point in the extended part and the occurrence frequency of the extreme point in the temperature data sequence. In one embodiment, the calculation expression of the preference degree is:
式中，表示延拓部分数据序列的优选程度，表示所述温度数据序列中极值点的出现频率，表示延拓部分数据序列中极值点的出现频率，为延拓部分数据序列中极值点出现频率与整个所述温度数据序列中极值点的出现频率的差值，表示延拓部分的极值点分布与整个温度数据序列的极值点分布的一致性；表示延拓部分数据序列中各个数据点的噪声表现程度的均值；B为超参数，防止分母为零。In the formula, Indicates the preference for extending part of the data sequence, represents the frequency of occurrence of extreme points in the temperature data sequence, Indicates the frequency of occurrence of extreme points in the extended data sequence, is the difference between the frequency of occurrence of extreme points in the extended data sequence and the frequency of occurrence of extreme points in the entire temperature data sequence, Indicates the consistency of the extreme point distribution of the extended part with the extreme point distribution of the entire temperature data series; It represents the mean of the noise performance of each data point in the extended part of the data sequence; B is a hyperparameter to prevent the denominator from being zero.
在一个实施例中，数据序列中极值点的出现频率可依据相邻极值点对应的时间差，以及相邻极值点的噪声表现程度之和进行计算，且与所述时间差以及所述噪声表现程度之和均呈负相关；所述相邻极值点对应的时间差是指相邻的两个极值点对应的采集时刻之间的差值，即相邻数据点对应的采集时刻之间的时长。数据序列中极值点的出现频率计算表达式为：In one embodiment, the frequency of occurrence of extreme value points in a data sequence can be calculated based on the time difference corresponding to adjacent extreme value points and the sum of the noise performance levels of adjacent extreme value points, and is negatively correlated with the time difference and the sum of the noise performance levels; the time difference corresponding to adjacent extreme value points refers to the difference between the acquisition times corresponding to two adjacent extreme value points, that is, the duration between the acquisition times corresponding to adjacent data points. The expression for calculating the frequency of occurrence of extreme value points in a data sequence is:
式中，表示数据序列中极值点的出现频率，N表示数据序列中的极值点个数，、分别表示数据序列中第j个极值点和第j+1个极值点对应的时间，、分别表示数据序列中第j个极值点对应的温度数据点的噪声表现程度和第j+1个极值点对应的温度数据点的噪声表现程度。若数据序列为延拓部分数据序列，则第j个极值点对应的温度数据点是指与该极值点关于延拓中心对称的温度数据序列中的数据点。In the formula, represents the frequency of occurrence of extreme points in the data sequence, N represents the number of extreme points in the data sequence, , They represent the time corresponding to the jth extreme point and the j+1th extreme point in the data sequence respectively. , They respectively represent the noise performance degree of the temperature data point corresponding to the jth extreme point in the data sequence and the noise performance degree of the temperature data point corresponding to the j+1th extreme point. If the data sequence is an extended partial data sequence, the temperature data point corresponding to the jth extreme point refers to the data point in the temperature data sequence that is symmetrical with the extreme point about the extended center.
相邻数据点对应的时间差越大，表明出现一个极值点后出现下一个极值点所花的时间越长，极值点出现频率越低；极值点的噪声表现程度越大，说明该极值点为噪声的可能性越大，即该极值点为假极值点的可能性越大，因此，相邻极值点的噪声表现程度之和越大，对应的极值点出现频率越低。故此，采用本实施例中的数据序列中极值点的出现频率计算表达式可准确高效地计算出极值点的出现频率。The larger the time difference corresponding to adjacent data points, the longer it takes for the next extreme point to appear after an extreme point appears, and the lower the frequency of extreme point appearance; the greater the noise performance of the extreme point, the greater the possibility that the extreme point is noise, that is, the greater the possibility that the extreme point is a false extreme point, therefore, the greater the sum of the noise performance of adjacent extreme points, the lower the frequency of corresponding extreme points. Therefore, the frequency calculation expression of extreme point appearance in the data sequence in this embodiment can accurately and efficiently calculate the frequency of extreme point appearance.
如图4所示，所述温度数据点的噪声表现程度计算方法包括：As shown in FIG4 , the method for calculating the noise performance degree of the temperature data point includes:
S401、依据所述温度数据序列获取该温度数据点的邻域范围内的温度数据序列，并将其记为邻域温度数据序列。S401. Obtain a temperature data sequence within a neighborhood range of the temperature data point according to the temperature data sequence, and record it as a neighborhood temperature data sequence.
本实施例中，在所述温度数据序列中以该温度数据点为中心的多个数据构成的数据序列即为该温度数据点的邻域温度数据序列。In this embodiment, in the temperature data sequence, a data sequence consisting of a plurality of data centered at the temperature data point is the neighborhood temperature data sequence of the temperature data point.
S402、获取邻域压力数据序列，具体为：获取所述邻域温度数据序列对应的时间段内各个温度采集时刻的氢化反应器内的压力，从而形成邻域压力数据序列。S402, obtaining a neighborhood pressure data sequence, specifically: obtaining the pressure in the hydrogenation reactor at each temperature collection moment within a time period corresponding to the neighborhood temperature data sequence, thereby forming a neighborhood pressure data sequence.
所述邻域温度数据序列对应的时间段是指该邻域温度数据序列中第一个温度数据的采集时刻至最后一个温度数据的采集时刻之间的时间段。The time period corresponding to the neighborhood temperature data sequence refers to the time period between the collection time of the first temperature data and the collection time of the last temperature data in the neighborhood temperature data sequence.
S403、计算该温度数据点的噪声表现程度，具体为：依据所述邻域温度数据序列与所述邻域压力数据序列之间的皮尔逊相关系数和该温度数据点的突变程度计算该温度数据点的噪声表现程度，且该温度数据点的噪声表现程度与所述皮尔逊相关系数呈负相关，与对应的突变程度呈正相关；所述突变程度用于表征该温度数据点的波动幅度。S403. Calculate the noise performance degree of the temperature data point, specifically: calculate the noise performance degree of the temperature data point based on the Pearson correlation coefficient between the neighborhood temperature data sequence and the neighborhood pressure data sequence and the mutation degree of the temperature data point, and the noise performance degree of the temperature data point is negatively correlated with the Pearson correlation coefficient and positively correlated with the corresponding mutation degree; the mutation degree is used to characterize the fluctuation amplitude of the temperature data point.
在本实施例中，该温度数据点的突变程度计算方法包括：In this embodiment, the method for calculating the mutation degree of the temperature data point includes:
a、获取温度数据点的线性取值，具体为：获取该温度数据点的前一个数据点和后一个数据点所在直线对应的函数，进而获取所述函数在该温度数据点对应的采集时刻处的取值，将其记为该温度数据点的线性取值。a. Obtain the linear value of the temperature data point, specifically: obtain the function corresponding to the straight line where the previous data point and the next data point of the temperature data point are located, and then obtain the value of the function at the collection time corresponding to the temperature data point, and record it as the linear value of the temperature data point.
如图5所示，假设该温度数据点为数据点H，其前一个数据点和后一个数据点分别为数据点P和数据点Q，数据点H对应的采集时刻为t2，对应的温度值为T2；数据点P对应的采集时刻为，对应的温度值为T1；数据点Q对应的采集时刻为，对应的温度值为T3。以采集时刻为横轴，以对应的温度值为纵轴建立直角坐标系，则数据点H、数据点P和数据点Q对应的坐标分别为（，）；假设数据点P和数据点Q所在直线对应的函数为f(t)，则数据点H的线性取值为f(t2)。As shown in Figure 5, assume that the temperature data point is data point H, and its previous data point and next data point are data point P and data point Q respectively. The collection time corresponding to data point H is t2, and the corresponding temperature value is T2; the collection time corresponding to data point P is , the corresponding temperature value is T1; the collection time corresponding to the data point Q is , the corresponding temperature value is T3. A rectangular coordinate system is established with the acquisition time as the horizontal axis and the corresponding temperature value as the vertical axis. The coordinates corresponding to data point H, data point P and data point Q are ( , ); Assuming that the function corresponding to the straight line where data point P and data point Q are located is f(t), then the linear value of data point H is f(t2).
b、获取温度数据点的突变程度，具体为：计算该温度数据点的线性取值和该温度数据点的实际取值之间的差值，并将其记为该温度数据点的突变程度。b. Obtain the mutation degree of the temperature data point, specifically: calculate the difference between the linear value of the temperature data point and the actual value of the temperature data point, and record it as the mutation degree of the temperature data point.
事实上，该温度数据点偏离该线段的中点的程度即为该温度数据点的突变程度。In fact, the degree to which the temperature data point deviates from the midpoint of the line segment is the mutation degree of the temperature data point.
因为在对称延拓法中，端点处的点为对称点不参与延拓，因此在本发明的碳酸锂生产自动化控制方法中不需要计算所述温度数据序列的左右两个端点处的噪声表现程度。Because in the symmetric continuation method, the points at the endpoints are symmetrical points and do not participate in the continuation, it is not necessary to calculate the noise performance degree at the left and right endpoints of the temperature data sequence in the lithium carbonate production automation control method of the present invention.
温度传感器的采集得到的温度数据中通常会混有噪声，噪声点通常表现为突变点，因此可利用温度数据点的突变程度计算得到其噪声表现程度。但是在氢化分解过程中会涉及吸热反应和放热反应（即Li2CO3+CO2+H2O→2LiHCO3为吸热反应，发生这种反应时温度下降，同时由化学方式，反应过程中吸收了二氧化碳，导致容器内压力降低，而2LiHCO3→Li2CO3↓+CO2↑+H2O为放热反应，发生这种反应时温度上升同时放出气体导致压力上升），这样的吸热或放热反应会导致温度数据中出现突变点，但这种突变点为正常的数据变化，因此需要利用温度数据和压力数据的皮尔逊相关系数（即线性相关程度）对噪声表现程度进行修正，皮尔逊相关系数的取值范围为[-1，+1]，相皮尔逊关系数的值越大，表明温度数据的突变越有可能是压力数据的突变导致的，温度数据的噪声表现程度越小，相反，相关系数的值越小，表明温度数据的突变越有可能是温度数据的噪声引起的，温度数据的噪声表现程度越大，因此，皮尔逊相关系数的值越大，修正后的温度数据的噪声表现程度越小，皮尔逊相关系数越小，修正后的温度数据的噪声表现程度越大。故此，采用本实施例的温度数据点的噪声表现程度计算方法可较为准确且高效地计算出温度数据点的噪声表现程度。The temperature data collected by the temperature sensor is usually mixed with noise, and the noise point usually appears as a mutation point. Therefore, the degree of noise performance can be calculated by using the mutation degree of the temperature data point. However, the hydrogenation decomposition process involves endothermic reactions and exothermic reactions (i.e. Li 2 CO3+CO 2 +H 2 O→2LiHCO 3 is an endothermic reaction. When this reaction occurs, the temperature drops. At the same time, carbon dioxide is absorbed by the reaction chemically, resulting in a decrease in the pressure in the container, and 2LiHCO 3 →Li 2 CO 3 ↓+CO 2 ↑+H 2 O is an exothermic reaction. When this reaction occurs, the temperature rises and gas is released, causing the pressure to rise). Such endothermic or exothermic reactions will cause mutation points in the temperature data, but this mutation point is a normal data change. Therefore, it is necessary to use the Pearson correlation coefficient (i.e., the degree of linear correlation) of the temperature data and the pressure data to correct the noise performance. The value range of the Pearson correlation coefficient is [-1, +1]. The larger the value of the Pearson correlation coefficient, the more likely it is that the mutation of the temperature data is caused by the mutation of the pressure data, and the smaller the degree of noise performance of the temperature data. On the contrary, the smaller the value of the correlation coefficient, the more likely it is that the mutation of the temperature data is caused by the noise of the temperature data, and the greater the degree of noise performance of the temperature data. Therefore, the larger the value of the Pearson correlation coefficient, the smaller the degree of noise performance of the corrected temperature data, and the smaller the Pearson correlation coefficient, the greater the degree of noise performance of the corrected temperature data. Therefore, the noise performance degree calculation method of the temperature data point of this embodiment can calculate the noise performance degree of the temperature data point more accurately and efficiently.
由以上实施例可知，某个温度数据点的噪声表现程度与对应的皮尔逊相关系数呈负相关，与该温度数据点的突变程度呈正相关，在一个实施例中，某个温度数据点的噪声表现程度计算表达式为：It can be seen from the above embodiments that the noise performance degree of a certain temperature data point is negatively correlated with the corresponding Pearson correlation coefficient, and positively correlated with the mutation degree of the temperature data point. In one embodiment, the calculation expression of the noise performance degree of a certain temperature data point is:
式中，表示第i个温度数据点的噪声表现程度，exp()为指数函数；为第i个温度数据点处的线性取值，表示第i个温度数据点的实际数值，则表示第i个温度数据点的突变程度。表示第i个温度数据点的邻域温度数据序列，为第i个温度数据点的邻域压力数据序列，表示邻域温度数据序列与邻域压力数据序列之间的皮尔逊相关系数。In the formula, Indicates the noise performance of the i-th temperature data point, exp() is an exponential function; is the linear value at the i-th temperature data point, represents the actual value of the ith temperature data point, then Indicates the degree of mutation of the i-th temperature data point. represents the neighborhood temperature data sequence of the i-th temperature data point, is the neighborhood pressure data sequence of the ith temperature data point, Represents the Pearson correlation coefficient between the neighborhood temperature data series and the neighborhood pressure data series.
因为皮尔逊相关系数的取值范围为-1到1，为了满足相关性的大小关系且便于归一化，则用加1将其限定为正数，即，然后即为对噪声表现程度的修正过程，皮尔逊相关系数越高，则噪声表现程度越小，故此，采用本实施例中的噪声表现程度计算表达式可较为准确且高效地计算出温度数据点的噪声表现程度。Because the value range of the Pearson correlation coefficient is -1 to 1, in order to satisfy the magnitude relationship of the correlation and facilitate normalization, it is limited to a positive number by adding 1, that is, ,Then That is, it is a correction process for the noise performance degree. The higher the Pearson correlation coefficient, the smaller the noise performance degree. Therefore, the noise performance degree calculation expression in this embodiment can be used to more accurately and efficiently calculate the noise performance degree of the temperature data point.
碳酸锂生产自动化控制系统实施例：Lithium carbonate production automation control system example:
本发明还提供了一种碳酸锂生产自动化控制系统。如图6所示，所述碳酸锂生产自动化控制系统包括处理器和存储器，所述存储器存储有计算机程序指令，当所述计算机程序指令被所述处理器执行时实现根据本发明第一方面所述的碳酸锂生产自动化控制方法。The present invention also provides a lithium carbonate production automation control system. As shown in Figure 6, the lithium carbonate production automation control system includes a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the lithium carbonate production automation control method according to the first aspect of the present invention is implemented.
所述碳酸锂生产自动化控制系统还包括通信总线和通信接口等本领域技术人员熟知的其他组件，其设置和功能为本领域中已知，因此在此不再赘述。The lithium carbonate production automation control system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose settings and functions are known in the art and will not be described in detail here.
在本发明中，前述的存储器可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。例如，计算机可读存储介质可以是任何适当的磁存储介质或者磁光存储介质，比如，阻变式存储器RRAM（Resistive RandomAccess Memory）、动态随机存取存储器DRAM（Dynamic Random Access Memory）、静态随机存取存储器SRAM（Static Random-Access Memory）、增强动态随机存取存储器EDRAM（Enhanced Dynamic Random Access Memory）、高带宽内存HBM（High-Bandwidth Memory）、混合存储立方HMC（Hybrid Memory Cube）等等，或者可以用于存储所需信息并且可以由应用程序、模块或两者访问的任何其他介质。任何这样的计算机存储介质可以是设备的一部分或可访问或可连接到设备。本发明描述的任何应用或模块可以使用可以由这样的计算机可读介质存储或以其他方式保持的计算机可读/可执行指令来实现。In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable/executable instructions that may be stored or otherwise maintained by such a computer-readable medium.
在本说明书的描述中，“多个”、“若干个”的含义是至少两个，例如两个，三个或更多个等，除非另有明确具体的限定。In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.
虽然本说明书已经示出和描述了本发明的多个实施例，但对于本领域技术人员显而易见的是，这样的实施例只是以示例的方式提供的。本领域技术人员会在不偏离本发明思想和精神的情况下想到许多更改、改变和替代的方式。应当理解的是在实践本发明的过程中，可以采用对本文所描述的本发明实施例的各种替代方案。Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.
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