Grade standard setting method and system for electrolytic cell and electronic equipment

Abstract

The invention relates to the field of aluminum electrolysis cells, and discloses a grade standard setting method and system for an electrolysis cell and electronic equipment. According to the method, an index scoring standard corresponding to each operation index is formulated according to historical data corresponding to each operation index of the aluminum electrolysis cell through an established data classification model, so that comprehensive scoring is performed on the historical data of the aluminum electrolysis cell according to the index scoring standards, and a historical comprehensive score is obtained; scoring intervals corresponding to different electrolytic cell grades of the aluminum electrolytic cell are formulated according to historical comprehensive scores through a data classification model, so that grade standard formulation of the aluminum electrolytic cell is achieved, the data classification model is established according to a normal distribution theory, and data are effectively classified according to the dispersion degree of the data; and subjective influence caused by experience judgment is eliminated, and an index scoring standard and an electrolytic cell grade standard are sequentially formulated through the data classification model, so that a user can conveniently formulate the grade standard of the aluminum electrolytic cell.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum electrolytic cells, and in particular to a method, system and electronic equipment for formulating grade standards for electrolytic cells. Background Art

[0002] Under the background of accelerated development of global industrialization and modernization, the aluminum electrolysis industry, as a key link in the modern metallurgical industry, has become the focus of the industry in terms of its technological development and production efficiency improvement. The core of the aluminum electrolysis industry lies in the aluminum reduction cell, which is a highly complex electrochemical system. Its operation directly determines the aluminum production efficiency, energy consumption and the quality of the final product.

[0003] During the aluminum electrolysis process, the operating state of the electrolytic cell is affected by many factors, including but not limited to temperature, current density, electrolyte composition, anode and cathode consumption rates, etc. The changes in these parameters not only directly affect the output of aluminum, but also the service life and overall energy consumption level of the electrolytic cell. Therefore, it is crucial to carry out refined management and control of the electrolytic cell to ensure its stable and efficient operation in order to improve the overall efficiency of the aluminum electrolysis industry.

[0004] With the rapid development of industrial Internet technology, the aluminum electrolysis industry has ushered in new opportunities for change. The industrial Internet provides real-time monitoring and precise analysis of the operating status of the electrolytic cell through technical means such as real-time data collection, remote monitoring, and big data analysis. Through the industrial Internet platform, various operating parameters of the electrolytic cell, such as current, voltage, temperature, electrolyte composition, etc., can be obtained in real time, and in-depth analysis and mining can be carried out, thereby providing a scientific basis for the performance evaluation, fault warning and optimized operation of the electrolytic cell.

[0005] However, with the continuous advancement of aluminum electrolysis industry technology, the types of parameter indicators collected are increasing, and each indicator has its own unique classification standard. At present, these classification standards are mostly based on the experience of workers and lack uniformity and accuracy. In addition, during the upgrading of electrolytic cells, different electrolytic cell design schemes, lining materials, thermal insulation and heat dissipation measures, and differences in series current intensity have led to large differences in process characteristic data. This makes the formulation of aluminum electrolytic cell grade standards extremely difficult, and it is impossible to accurately classify and judge the production grade of the electrolytic cell during production, and thus it is impossible to provide effective guidance for production. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] In view of the above-mentioned shortcomings of the prior art, the present application discloses a method, system and electronic equipment for formulating grade standards for electrolytic cells to improve the accuracy of judging the production grade of aluminum electrolytic cells.

[0008] The present application discloses a method for formulating a grade standard for an electrolytic cell, comprising: pre-establishing a data classification model, wherein the data classification model is used to determine a valid numerical range according to a data mean value and a data standard deviation corresponding to target data, dividing the valid numerical range into numerical sub-areas corresponding to a plurality of data levels, and determining a target ratio corresponding to each data level according to a data ratio corresponding to each numerical sub-area, so as to determine a target numerical range corresponding to each data level from the valid numerical range according to the target ratio; obtaining historical data corresponding to a plurality of operating indicators of an aluminum electrolytic cell; inputting the historical data corresponding to the same operating indicator as target data into the data classification model to obtain an indicator scoring standard, and scoring the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standard corresponding to each operating indicator to obtain a historical comprehensive score corresponding to the aluminum electrolytic cell; inputting a plurality of historical comprehensive scores as target data into the data classification model to obtain an electrolytic cell grade standard corresponding to the aluminum electrolytic cell, wherein the electrolytic cell grade standard includes scoring ranges corresponding to a plurality of electrolytic cell grades.

[0009] In one embodiment of the present application, the valid numerical range is determined according to the data mean value and the data standard deviation corresponding to the target data in the following manner: the data standard deviation is calculated according to a preset upper limit multiplier and a preset lower limit multiplier, respectively, to obtain an upper limit value corresponding to the preset upper limit multiplier and a lower limit value corresponding to the preset lower limit multiplier; the data mean value is calculated according to the upper limit value and the lower limit value, respectively, to obtain a valid range maximum value corresponding to the upper limit value and a valid range minimum value corresponding to the lower limit value; the valid numerical range corresponding to the target data is determined according to the valid range maximum value and the valid range minimum value.

[0010] In one embodiment of the present application, the target ratio corresponding to each of the data levels is determined according to the data ratio corresponding to each of the numerical sub-areas in the following manner: quantitative statistics are performed according to the target data in the valid numerical interval to obtain the effective data volume, and quantitative statistics are performed according to the target data corresponding to each of the numerical sub-intervals to obtain the interval data volume corresponding to each of the numerical sub-intervals; the effective data volume is calculated according to the interval data volume to obtain the data ratio corresponding to each of the data sub-intervals; if the ratio adjustment mode includes a recommended ratio mode, the data ratio corresponding to each of the numerical sub-intervals is displayed to the user, and the received user adjustment instructions are executed to obtain the target ratio corresponding to each of the data levels; if the ratio adjustment mode includes an expected ratio mode, the user expected ratio corresponding to each of the numerical sub-intervals is obtained, and the target ratio corresponding to each of the data levels is obtained according to the data ratio corresponding to the same data sub-interval and the user expected ratio.

[0011] In one embodiment of the present application, historical data corresponding to multiple operating indicators of an aluminum electrolytic cell are obtained, including: obtaining an evaluation dimension corresponding to the aluminum electrolytic cell, wherein the evaluation dimension includes at least one of a production capacity dimension, an energy-saving dimension, an efficiency dimension, and a safety dimension; setting operating indicators according to the evaluation dimensions, and setting a data collection cycle corresponding to the aluminum electrolytic cell, wherein the operating indicators corresponding to the production capacity dimension include current efficiency, the operating indicators corresponding to the energy-saving dimension include one or more of a set voltage, a DC power consumption, an electrolyte temperature, and a noise level, the operating indicators corresponding to the efficiency dimension include an aluminum level and / or a molecular ratio, and the operating indicators corresponding to the safety dimension include an electrolyte temperature and / or a noise level; collecting indicator data corresponding to the aluminum electrolytic cell in different data collection cycles according to each of the operating indicators; determining abnormal data from the historical parameters corresponding to each of the operating indicators according to a preset abnormal ratio, and determining the crawling period data corresponding to the aluminum electrolytic cell in the crawling period from the historical parameters corresponding to each of the operating indicators; deleting the abnormal data and the crawling period data from the indicator data to obtain historical data.

[0012] In one embodiment of the present application, historical data corresponding to the same operating indicator is input as target data into the data classification model to obtain an indicator scoring standard, including: determining each of the operating indicators as a target indicator; in response to the target indicator, obtaining an assignment rule corresponding to the target indicator to generate a plurality of indicator evaluation scores corresponding to the target indicator according to the assignment rule, wherein the assignment rule is used to characterize the correlation between historical data and the indicator evaluation score; using the indicator evaluation score as the data level corresponding to the target indicator, inputting the historical data corresponding to the target indicator as target data into the data classification model, and using the target numerical range corresponding to each of the indicator evaluation scores as the indicator scoring standard corresponding to the target indicator.

[0013] In one embodiment of the present application, the historical data corresponding to the aluminum electrolytic cell is scored according to the indicator scoring standards corresponding to each of the operating indicators to obtain a historical comprehensive score corresponding to the aluminum electrolytic cell, including: obtaining the indicator weights corresponding to each of the operating indicators; determining the highest indicator scores corresponding to each of the operating indicators from the indicator scoring standards, and performing weighted calculation on the highest scores of each indicator according to the indicator weights to obtain a total indicator score; matching the historical data corresponding to the aluminum electrolytic cell from each of the indicator scoring standards to obtain the historical scores corresponding to each of the operating indicators, and performing weighted calculation on each of the historical scores according to the indicator weights to obtain a historical total score; calculating the total indicator score according to the historical total score to obtain a historical comprehensive score of the aluminum electrolytic cell.

[0014] In one embodiment of the present application, after a plurality of historical comprehensive scores are input as target data into the data classification model to obtain the electrolytic cell grade standard corresponding to the aluminum electrolytic cell, the method further includes: obtaining current data corresponding to the aluminum electrolytic cell; scoring the current data according to the indicator scoring standards corresponding to each of the operating indicators to obtain a current comprehensive score; and classifying the current comprehensive score according to the electrolytic cell grade standard to determine the current grade corresponding to the aluminum electrolytic cell from among the electrolytic cell grades.

[0015] In one embodiment of the present application, the method further includes: displaying at least a part of the current data, the current comprehensive score, and the current level corresponding to the aluminum electrolysis cell as display content; adjusting the current data corresponding to the aluminum electrolysis cell according to the display content to control the aluminum electrolysis cell to change from the current level to the target level, wherein the levels of the electrolysis cells are arranged in order from best to worst, and the target level is before the current level.

[0016] The present application discloses a system for formulating grade standards for electrolytic cells, comprising: an establishment module for establishing a data classification model, wherein the data classification model is used to determine a valid numerical interval according to a data mean value and a data standard deviation corresponding to target data, divide the valid numerical interval into numerical sub-regions corresponding to a plurality of data levels, and determine a target ratio corresponding to each data level according to a data ratio corresponding to each numerical sub-region, so as to determine a target numerical interval corresponding to each data level from the valid numerical interval according to the target ratio; an acquisition module for acquiring historical data corresponding to a plurality of operating indicators of an aluminum electrolytic cell; a first classification module for inputting historical data corresponding to the same operating indicator as target data into the data classification model to obtain an indicator scoring standard, and scoring the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standard corresponding to each operating indicator to obtain a historical comprehensive score corresponding to the aluminum electrolytic cell; a second classification module for inputting a plurality of historical comprehensive scores as target data into the data classification model to obtain an electrolytic cell grade standard corresponding to the aluminum electrolytic cell, wherein the electrolytic cell grade standard includes scoring intervals corresponding to a plurality of electrolytic cell grades.

[0017] The present application discloses an electronic device, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the above method.

[0018] Beneficial effects of this application:

[0019] Through the established data classification model, the indicator scoring standard corresponding to each operating indicator is first formulated according to the historical data corresponding to each operating indicator of the aluminum electrolytic cell, so as to comprehensively score the historical data of the aluminum electrolytic cell according to the indicator scoring standard, and obtain the historical comprehensive score. Then, the scoring intervals corresponding to the aluminum electrolytic cell at different electrolytic cell levels are formulated according to the historical comprehensive score through the data classification model, so as to realize the formulation of the grade standard of the aluminum electrolytic cell. In this way, the data classification model is established based on the normal distribution theory, the valid data interval is determined according to the data mean and data standard deviation, and the natural stratification boundary is defined according to the valid data interval, objectively reflecting the deviation between the data value and its mean value, effectively classifying the data according to its dispersion degree, and eliminating the subjective influence caused by experience judgment. Then, the indicator scoring standard corresponding to the operating indicator and the electrolytic cell grade standard corresponding to the aluminum electrolytic cell are formulated in turn through the data classification model, which is convenient for users to formulate the grade standard of the aluminum electrolytic cell, and improve the accuracy of judging the production grade of the aluminum electrolytic cell, and provide effective guidance for production. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1is a schematic flow chart of a method for formulating a grade standard for an electrolytic cell in an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a setting page of an assignment rule in an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of a target ratio adjustment page in an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of a setting page for an indicator weight in an embodiment of the present invention;

[0024] Figure 5 is a schematic diagram of a storage interface of an electrolytic cell grade standard in an embodiment of the present invention;

[0025] Figure 6 is a schematic diagram of an evaluation interface of an electrolytic cell in an embodiment of the present invention;

[0026] Figure 7 is a schematic diagram of a trend interface of an electrolytic cell in an embodiment of the present invention;

[0027] Figure 8 is a schematic diagram of a transition interface between different levels of an electrolytic cell in an embodiment of the present invention;

[0028] Fig. 9 is a flow chart of another method for formulating grade standards for electrolytic cells according to an embodiment of the present invention;

[0029] Fig.10 is a structural schematic diagram of a system for formulating grade standards for electrolytic cells in an embodiment of the present invention;

[0030] Fig.11 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and sub-samples in the embodiments can be combined with each other without conflict.

[0032] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0033] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0034] The terms "first", "second", etc. in the specification and claims of the embodiments 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 terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0035] Unless otherwise stated, the term "plurality" means two or more.

[0036] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0037] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0038] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for formulating a grade standard for an electrolytic cell, comprising:

[0039] Step S101, pre-establishing a data classification model;

[0040] The data classification model is used to determine the valid numerical interval according to the data mean value and the data standard deviation corresponding to the target data, divide the valid numerical interval into numerical sub-areas corresponding to a plurality of data levels, and determine the target proportion corresponding to each data level according to the data proportion corresponding to each numerical sub-area, so as to determine the target numerical interval corresponding to each data level from the valid numerical interval according to the target proportion;

[0041] Step S102, obtaining historical data corresponding to a plurality of operating indicators of the aluminum reduction cell;

[0042] Step S103, inputting the historical data corresponding to the same operating indicator into the data classification model as the target data to obtain the indicator scoring standard, and scoring the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standard corresponding to each operating indicator to obtain the historical comprehensive score corresponding to the aluminum electrolytic cell;

[0043] Step S104, inputting a plurality of historical comprehensive scores as target data into a data classification model to obtain an electrolytic cell grade standard corresponding to the aluminum electrolytic cell;

[0044] Among them, the electrolytic cell grade standard includes scoring ranges corresponding to multiple electrolytic cell grades.

[0045] The method for formulating the grade standard of the electrolytic cell provided by the embodiment of the present disclosure is adopted. The index scoring standard corresponding to each operating index of the aluminum electrolytic cell is respectively formulated according to the historical data corresponding to each operating index of the aluminum electrolytic cell through the established data classification model, so as to comprehensively score the historical data of the aluminum electrolytic cell according to the index scoring standard to obtain the historical comprehensive score, and then the scoring intervals corresponding to the aluminum electrolytic cell at different electrolytic cell levels are formulated according to the historical comprehensive score through the data classification model, so as to achieve the formulation of the grade standard of the aluminum electrolytic cell. In this way, a data classification model is established based on the normal distribution theory, the valid data interval is determined according to the data mean and the data standard deviation, and the natural stratification boundary is defined according to the valid data interval, objectively reflecting the deviation between the data value and its mean value, effectively classifying the data according to its dispersion degree, and eliminating the subjective influence caused by the empirical judgment, and then the index scoring standard corresponding to the operating index and the electrolytic cell grade standard corresponding to the aluminum electrolytic cell are formulated in sequence through the data classification model, so as to facilitate the user to formulate the grade standard of the aluminum electrolytic cell, improve the accuracy of judging the production grade of the aluminum electrolytic cell, and provide effective guidance for production.

[0046] Optionally, historical data corresponding to multiple operating indicators of the aluminum electrolytic cell are obtained, including: obtaining evaluation dimensions corresponding to the aluminum electrolytic cell, wherein the evaluation dimensions include at least one of a production capacity dimension, an energy-saving dimension, an efficiency dimension, and a safety dimension; setting operating indicators according to the evaluation dimensions, and setting a data collection period corresponding to the aluminum electrolytic cell, wherein the operating indicators corresponding to the production capacity dimension include current efficiency, the operating indicators corresponding to the energy-saving dimension include one or more of a set voltage, a DC power consumption, an electrolyte temperature, and a noise level, the operating indicators corresponding to the efficiency dimension include an aluminum level and / or a molecular ratio, and the operating indicators corresponding to the safety dimension include an electrolyte temperature and / or a noise level; collecting indicator data corresponding to the aluminum electrolytic cell in different data collection periods according to each operating indicator; determining abnormal data from historical parameters corresponding to each operating indicator according to a preset abnormal ratio, and determining the crawling period data corresponding to the aluminum electrolytic cell in the crawling period from the historical parameters corresponding to each operating indicator; deleting the abnormal data and crawling period data from the indicator data to obtain historical data.

[0047] In some embodiments, for the electrolytic cell to be managed and analyzed, extensive data collection is performed according to different evaluation dimensions, including current efficiency, set voltage, aluminum level, molecular ratio, voltage and power consumption index, thermal stability index, cell condition stability index and furnace condition index, etc., so as to ensure sufficient data to support subsequent analysis. Among them, some operating indicators are shown in Table 1.

[0048] Table 1

[0049]

[0050]

[0051] In some embodiments, aluminum electrolytic cells are screened by screening and adjusting prices by organizational structure, electrolytic cell design type, etc., wherein the organizational structure includes designated workshops, designated plant areas, etc.; setting data collection cycles, indicator classification quantity (C), data collection time range (t0-t1t fin ), abnormal proportion (d) and climbing period (k), and extract data from the screened aluminum electrolytic cells according to the data collection period, the number of indicator categories, the time range of data collection, the abnormal proportion and the climbing period to obtain historical data. For example, historical data of the aluminum electrolytic cells within one year are obtained, wherein one historical data is the data within the data collection period (days).

[0052] In some embodiments, abnormal data is determined from historical parameters corresponding to each operating indicator according to a preset abnormal ratio, including: if the abnormal ratio is set to 2.5%, the historical data is arranged from small to large, and the 2.5% ratio data at the front and the 2.5% ratio data at the back are taken as abnormal data.

[0053] In some embodiments, the climbing period of the electrolytic cell involves the process from the initial startup to the stable operation of the electrolytic cell, during which fine control and management are required to ensure the stability and efficiency of the electrolysis process. Therefore, the production operations of the electrolytic cell are frequent and the collected data fluctuates greatly. The climbing period data corresponding to the aluminum electrolytic cell during the climbing period needs to be excluded from the production data.

[0054] Optionally, historical data corresponding to the same operating indicator is input as target data into a data classification model to obtain an indicator scoring standard, including: determining each operating indicator as a target indicator; in response to the target indicator, obtaining an assignment rule corresponding to the target indicator to generate multiple indicator evaluation scores corresponding to the target indicator according to the assignment rule, wherein the assignment rule is used to characterize the correlation between historical data and the indicator evaluation score; using the indicator evaluation score as the data level corresponding to the target indicator, inputting the historical data corresponding to the target indicator as target data into the data classification model, and using the target numerical range corresponding to each indicator evaluation score as the indicator scoring standard corresponding to the target indicator.

[0055] In some embodiments, the number of indicator categories of the target indicator is obtained, and an indicator evaluation score is generated according to the number of indicator categories. For example, if the number of indicator categories is set to 5, 5 indicator evaluation scores are generated, namely 1 point, 2 points, 3 points, 4 points and 5 points.

[0056] In some embodiments, if the assignment rule includes a positive correlation between data and scores, the larger the index evaluation score, the larger the corresponding target numerical range; if the assignment rule includes a negative correlation between data and scores, the larger the index evaluation score, the smaller the corresponding target numerical range.

[0057] In some embodiments, the setting page of the assignment rule is as follows: Figure 2 shown.

[0058] Optionally, the valid numerical range is determined according to the data mean and data standard deviation corresponding to the target data in the following manner: the data standard deviation is calculated according to a preset upper limit multiplier and a preset lower limit multiplier, respectively, to obtain an upper limit value corresponding to the preset upper limit multiplier and a lower limit value corresponding to the preset lower limit multiplier; the data mean is calculated according to the upper limit value and the lower limit value, respectively, to obtain a maximum value of the valid interval corresponding to the upper limit value and a minimum value of the valid interval corresponding to the lower limit value; the valid numerical range corresponding to the target data is determined according to the maximum value of the valid interval and the minimum value of the valid interval.

[0059] In some embodiments, calculations are performed based on historical data corresponding to the same operating indicator to obtain a data average value (μ) and a data standard deviation (σ); the target data is standardized according to the data average value and the data standard deviation according to a standard score calculation formula, wherein the standard score calculation formula includes standard score = (target data - μ) / σ.

[0060] Optionally, the target ratio corresponding to each data level is determined according to the data ratio corresponding to each numerical sub-area in the following manner: quantitatively counting the target data in the valid numerical interval to obtain the effective data volume, and quantitatively counting the target data corresponding to each numerical sub-interval to obtain the interval data volume corresponding to each numerical sub-interval; calculating the effective data volume according to the data volume of each interval to obtain the data ratio corresponding to each data sub-interval; if the ratio adjustment mode includes a recommended ratio mode, the data ratio corresponding to each numerical sub-interval is displayed to the user, and the received user adjustment instruction is executed to obtain the target ratio corresponding to each data level; if the ratio adjustment mode includes an expected ratio mode, the user expected ratio corresponding to each numerical sub-interval is obtained, and the target ratio corresponding to each data level is obtained by calculation according to the data ratio corresponding to the same data sub-interval and the user expected ratio.

[0061] In some embodiments, the upper limit multiplier and the lower limit multiplier are both set to 3 times, and the historical data within 3 times the data standard deviation of the data mean is taken as valid data, and the obtained valid numerical range is between (μ-3σ) and (μ+3σ).

[0062] In some embodiments, the valid numerical interval is evenly divided into multiple numerical sub-areas according to the number of indicator categories, and the data ratio (Pi) of the data in each numerical sub-area in the valid data is calculated; the data ratio is displayed to the user as recommended data for reference, and the user makes adjustments based on the recommended data to obtain the target ratio.

[0063] In some embodiments, the target ratio adjustment page is as follows: Figure 3 shown.

[0064] In some embodiments, if the ratio adjustment mode includes an expected ratio mode, the ratio weights corresponding to the user expected ratio and the data ratio are set respectively to calculate the target ratio based on the ratio weights. For example, the user expected ratio is 0.1, the data ratio is 0.06, the ratio weight corresponding to the user expected ratio is 70%, and the ratio weight corresponding to the data ratio is 30%. Then the target ratio = [(0.1×70%) + (0.06×30%)]×100% = 8.8%.

[0065] In some embodiments, valid data corresponding to each indicator evaluation score is determined according to the target ratio, and a target numerical range corresponding to each indicator evaluation score is formed, wherein the valid data corresponding to any indicator evaluation score is within the target numerical range corresponding to the indicator evaluation score.

[0066] In some embodiments, a total of 100 aluminum electrolytic cells are set; the number of indicator categories is set to 4; the time range for data collection is set from January 1, 2023 to April 11, 2023, with a total of 100 historical data for each electrolytic cell; abnormal data and climbing period data are deleted from the historical data; the valid numerical range is determined based on the data average value and data standard value of the historical data, and the valid numerical range is divided into 4 numerical sub-ranges, among which the data ratios corresponding to some operating indicators are shown in Table 2.

[0067] Table 2

[0068] Operational indicators Numeric subinterval 1 Numeric subinterval 2 Numeric subinterval 3 Numeric subinterval 4 Operating voltage 6％ 8％ 58％ 28％ Aluminum Level 13％ 41％ 41％ 5％ Current efficiency 58％ 24％ 5％ 13％

[0069] In some embodiments, the target ratio corresponding to each data level is determined according to the data ratio corresponding to each numerical sub-region; the target numerical interval corresponding to each data level is determined from the valid numerical interval according to the target ratio, and the indicator scoring standards corresponding to some operating indicators are obtained as shown in Table 3.

[0070] Table 3

[0071]

[0072] Optionally, historical data corresponding to the aluminum reduction cell is scored according to the indicator scoring standards corresponding to each operating indicator to obtain a historical comprehensive score corresponding to the aluminum reduction cell, including: obtaining the indicator weights corresponding to each operating indicator; determining the highest indicator scores corresponding to each operating indicator from the indicator scoring standards, and performing weighted calculation on the highest scores of each indicator according to the weights of each indicator to obtain a total indicator score; matching the historical data corresponding to the aluminum reduction cell from each indicator scoring standard to obtain historical scores corresponding to each operating indicator, and performing weighted calculation on each historical score according to the weights of each indicator to obtain a historical total score; calculating the total indicator score according to the historical total score to obtain a historical comprehensive score of the aluminum reduction cell.

[0073] In some embodiments, a default weight is set for each operating indicator through the AHP hierarchical weight decision analysis method, and an indicator importance score is set for each operating indicator according to the expert evaluation method to form a relative importance judgment matrix; based on the default weight and the relative importance judgment matrix, the indicator weights corresponding to each operating indicator are determined to ensure that the importance of each indicator is reasonably considered.

[0074] In some embodiments, the indicator weight setting page is as follows: Figure 4 shown.

[0075] In some embodiments, the indicator weights of some operating indicators are shown in Table 4.

[0076] Table 4

[0077] index Operating voltage Aluminum Level Current efficiency Weight 30％ 30％ 40％

[0078] In some embodiments, if the user selects a part of the operating indicators, normalization is performed according to the indicator weights corresponding to the selected operating indicators to obtain actual weights, wherein the sum of the actual weights is equal to 1.

[0079] In some embodiments, the historical score corresponding to the historical data of the aluminum electrolytic cell in any period is determined according to the indicator scoring standard corresponding to any operating indicator; the historical score corresponding to each operating indicator is weightedly calculated in combination with the indicator weight, and the historical total score is mapped to a percentage system to obtain a historical comprehensive score (score) of the aluminum electrolytic cell.

[0080] In some embodiments, the historical total score corresponding to a portion of historical data is shown in Table 5.

[0081] Table 5

[0082]

[0083] In some embodiments, the total score of the indicators is calculated according to the historical total score to obtain a historical comprehensive score of the aluminum electrolytic cell, wherein the historical comprehensive score corresponding to a portion of the historical data is shown in Table 6.

[0084] Table 6

[0085] Labeling of historical data Historical comprehensive score 1001 80 1002 47.5 1003 62.5

[0086] In some embodiments, multiple historical comprehensive scores are input into a data classification model as target data, and the valid numerical range is determined based on the corresponding data mean and data standard deviation of the historical comprehensive scores. The valid numerical range is divided into multiple numerical sub-areas corresponding to the data levels, and the data ratio corresponding to each value sub-area is shown in Table 7.

[0087] Table 7

[0088] Electrolyzer Grade Electrolyzer Level 1 Electrolyzer Level 2 Electrolyzer Level 3 Electrolyzer Level 4 Scale settings 16％ 11％ 57％ 16％

[0089] In some embodiments, the target ratio is determined according to each data ratio corresponding to the historical comprehensive score, so as to obtain the electrolytic cell grade standard corresponding to the aluminum electrolytic cell according to the target ratio corresponding to the historical comprehensive score, wherein the electrolytic cell grade standard is shown in Table 8.

[0090] Table 8

[0091] Electrolyzer Grade Electrolyzer Level 1 Electrolyzer Level 2 Electrolyzer Level 3 Electrolyzer Level 4 Rating range (82.5，100] (75，82.5] (65，75] [0，65]

[0092] Optionally, after inputting multiple historical comprehensive scores as target data into a data classification model to obtain an electrolytic cell grade standard corresponding to the aluminum electrolytic cell, the method also includes: obtaining current data corresponding to the aluminum electrolytic cell; scoring the current data according to the indicator scoring standards corresponding to each operating indicator to obtain a current comprehensive score; and classifying the current comprehensive score according to the electrolytic cell grade standard to determine the current grade corresponding to the aluminum electrolytic cell from among the electrolytic cell grades.

[0093] In some embodiments, an electrolytic cell grade standard is established based on historical data of all aluminum electrolytic cells to obtain a general grade standard; the aluminum electrolytic cells are divided into multiple cell types, and an electrolytic cell grade standard is established based on historical data corresponding to the same cell type to obtain a single-type grade standard; if the aluminum electrolytic cell to be evaluated can match the corresponding target type in each cell type, it is scored according to the single-type grade standard corresponding to the target type; if the aluminum electrolytic cell to be evaluated cannot match any cell type, it is scored according to the general grade standard.

[0094] In some embodiments, after obtaining the electrolytic cell grade standard corresponding to the aluminum electrolytic cell, the electrolytic cell will be scored based on this standard; after saving, the system stores the parameters set in all steps, cell classification standards, and scoring segmentation standards into a warehouse for use in cell classification; according to the above steps, the data of different time dimensions and statistical dimensions (overall, workshop, work area, single cell) of the standard are sorted, summarized and recorded for subsequent query and analysis.

[0095] In some embodiments, the storage interface of the electrolyzer grade standard is as follows Figure 5 shown.

[0096] Optionally, the method also includes: displaying at least a part of the current data, current comprehensive score, and current level corresponding to the aluminum electrolytic cell as display content; adjusting the current data corresponding to the aluminum electrolytic cell according to the display content to control the aluminum electrolytic cell to change from the current level to the target level, wherein the levels of the electrolytic cells are arranged in order from good to bad, and the target level is before the current level.

[0097] In some embodiments, an adaptive cell classification method is used, that is, when the user selects electrolytic cells of the same design type, the standards applicable to that type of electrolytic cells are used for evaluation; when the user selects multiple different types of electrolytic cells, general standards are used for evaluation.

[0098] In some embodiments, the evaluation interface of the electrolytic cell is as follows Figure 6 shown.

[0099] In some embodiments, the overall changes and classification changes of the electrolytic cell are analyzed by using the cell classification scores, and the changes of each indicator value in different time dimensions are displayed in a combined manner of data and graphics, and abnormal situations are marked. This helps to understand the overall status and trend of the electrolytic cell, analyze the details of the reasons for improvement or deterioration, and improve the indicators that have deteriorated in a targeted manner.

[0100] In some embodiments, the trend interface of the electrolytic cell is as follows Figure 7 shown.

[0101] In some embodiments, by analyzing the overall change trend of the electrolytic cells in the selected area at the beginning and end of the period through the category changes of the electrolytic cells at the beginning and end of the period, and realizing the analysis of the details of the category change through the Sankey diagram, it is possible to observe in detail which cells have changed from which categories to other categories (such as which cells are Class A at the beginning of the period, which are still Class A, and which have dropped to worse categories such as BCD).

[0102] In some embodiments, the transition interface between different levels of the electrolytic cell is as follows Figure 8 shown.

[0103] Combination Fig. 9As shown, the embodiment of the present disclosure provides a method for formulating a grade standard for an electrolytic cell, comprising:

[0104] Step S901, obtaining historical data corresponding to multiple operating indicators of the aluminum reduction cell;

[0105] Step S902, setting the indicator weights corresponding to each operating indicator;

[0106] Step S903, inputting historical data corresponding to the same operating indicator into the data classification model as target data;

[0107] Step S904, calculating the data mean value and the data standard deviation according to the historical data corresponding to the same operating indicator, and determining the valid value range according to the data mean value and the data standard deviation;

[0108] Step S905, dividing the target numerical ranges corresponding to the multiple indicator evaluation scores according to the valid numerical ranges corresponding to the operating indicators, and obtaining the indicator scoring standards corresponding to the operating indicators;

[0109] Step S906, scoring the historical data of the aluminum reduction cell according to the indicator weight and the indicator scoring standard to obtain the historical comprehensive score corresponding to the aluminum reduction cell;

[0110] Step S907, inputting multiple historical comprehensive scores into a data classification model as target data;

[0111] Step S908, calculating the data average and data standard deviation according to each historical comprehensive score, and determining the valid value range according to the data average and data standard deviation;

[0112] Step S909, dividing the score intervals corresponding to the multiple electrolytic cell levels according to the valid numerical intervals corresponding to the historical comprehensive scores;

[0113] Step S910, obtaining current data corresponding to the aluminum electrolysis cell;

[0114] Step S911, scoring the current data according to the indicator scoring standards corresponding to each operating indicator to obtain the current comprehensive score;

[0115] Step S912, classifying the current comprehensive score according to the electrolytic cell grade standard to determine the current grade corresponding to the aluminum electrolytic cell from each electrolytic cell grade;

[0116] Step S913, at least a part of the current data, current comprehensive score, and current level corresponding to the aluminum electrolytic cell is displayed as display content.

[0117] The method for formulating the grade standard of the electrolytic cell provided by the embodiment of the present disclosure is adopted. The index scoring standard corresponding to each operating index of the aluminum electrolytic cell is respectively formulated according to the historical data corresponding to each operating index of the aluminum electrolytic cell through the established data classification model, so as to comprehensively score the historical data of the aluminum electrolytic cell according to the index scoring standard to obtain the historical comprehensive score, and then the scoring intervals corresponding to the aluminum electrolytic cell at different electrolytic cell levels are formulated according to the historical comprehensive score through the data classification model, so as to achieve the formulation of the grade standard of the aluminum electrolytic cell. In this way, a data classification model is established based on the normal distribution theory, the valid data interval is determined according to the data mean and the data standard deviation, and the natural stratification boundary is defined according to the valid data interval, objectively reflecting the deviation between the data value and its mean value, effectively classifying the data according to its dispersion degree, and eliminating the subjective influence caused by the empirical judgment, and then the index scoring standard corresponding to the operating index and the electrolytic cell grade standard corresponding to the aluminum electrolytic cell are formulated in sequence through the data classification model, so as to facilitate the user to formulate the grade standard of the aluminum electrolytic cell, improve the accuracy of judging the production grade of the aluminum electrolytic cell, and provide effective guidance for production.

[0118] Combination Fig.10 As shown, an embodiment of the present disclosure provides a grade standard formulation system for electrolytic cells, including an establishment module 1001, an acquisition module 1002, a first classification module 1003 and a second classification module 1004.

[0119] Establishing module 1001 is used to establish a data classification model, wherein the data classification model is used to determine the valid numerical range according to the data mean and data standard deviation corresponding to the target data, divide the valid numerical range into numerical sub-areas corresponding to multiple data levels, and determine the target ratio corresponding to each data level according to the data ratio corresponding to each numerical sub-area, so as to determine the target numerical range corresponding to each data level from the valid numerical range according to the target ratio.

[0120] The acquisition module 1002 is used to obtain historical data corresponding to multiple operating indicators of the aluminum reduction cell.

[0121] The first classification module 1003 is used to input the historical data corresponding to the same operating indicator as the target data into the data classification model to obtain the indicator scoring standard, and score the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standard corresponding to each operating indicator to obtain the historical comprehensive score corresponding to the aluminum electrolytic cell.

[0122] The second classification module 1004 is used to input multiple historical comprehensive scores as target data into the data classification model to obtain the electrolytic cell grade standard corresponding to the aluminum electrolytic cell, wherein the electrolytic cell grade standard includes score intervals corresponding to multiple electrolytic cell grades.

[0123] The system for formulating the grade standard of the electrolytic cell provided by the embodiment of the present disclosure is adopted. The index scoring standard corresponding to each operating index of the aluminum electrolytic cell is respectively formulated according to the historical data corresponding to each operating index of the aluminum electrolytic cell through the established data classification model, so as to comprehensively score the historical data of the aluminum electrolytic cell according to the index scoring standard to obtain the historical comprehensive score, and then the scoring intervals corresponding to the aluminum electrolytic cell at different electrolytic cell levels are formulated according to the historical comprehensive score through the data classification model, thereby realizing the formulation of the grade standard of the aluminum electrolytic cell. In this way, a data classification model is established based on the normal distribution theory, the valid data interval is determined according to the data mean and the data standard deviation, and the natural stratification boundary is defined according to the valid data interval, objectively reflecting the deviation between the data value and its mean value, effectively classifying the data according to its dispersion degree, and eliminating the subjective influence caused by the empirical judgment, and then the index scoring standard corresponding to the operating index and the electrolytic cell grade standard corresponding to the aluminum electrolytic cell are formulated in sequence through the data classification model, so as to facilitate the user to formulate the grade standard of the aluminum electrolytic cell, improve the accuracy of judging the production grade of the aluminum electrolytic cell, and provide effective guidance for production.

[0124] An embodiment of the present disclosure further provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the above method.

[0125] Fig.11 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Fig.11 The computer system 1100 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0126] like Fig.11 As shown, the computer system 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1102 or the program loaded from the storage part 1108 to the random access memory (RAM) 1103, such as executing the method in the above embodiment. In the random access memory 1103, various programs and data required for system operation are also stored. The central processing unit 1101, the read-only memory 1102 and the random access memory 1103 are connected to each other through a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0127] The following components are connected to the input / output interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed so that a computer program read therefrom is installed into the storage section 1108 as needed.

[0128] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1109, and / or installed from a removable medium 1111. When the computer program is executed by a central processing unit (CPU) 1101, various functions defined in the system of the present application are executed.

[0129] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation can be changed. Parts and sub-samples of some embodiments may be included in or replace parts and sub-samples of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of a stated sub-sample, whole, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other sub-samples, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0130] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0131] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some sub-samples can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0132] The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for formulating a grade standard for an electrolytic cell, characterized in that: include: Pre-establishing a data classification model, wherein the data classification model is used to determine a valid numerical interval according to a data mean value and a data standard deviation corresponding to the target data, divide the valid numerical interval into a plurality of numerical sub-regions corresponding to the data levels, and determine a target ratio corresponding to each data level according to a data ratio corresponding to each numerical sub-region, so as to determine a target numerical interval corresponding to each data level from the valid numerical interval according to the target ratio; Obtain historical data corresponding to multiple operating indicators of aluminum electrolysis cells; Inputting historical data corresponding to the same operating indicator as target data into the data classification model to obtain an indicator scoring standard, and scoring the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standards corresponding to each of the operating indicators to obtain a historical comprehensive score corresponding to the aluminum electrolytic cell; A plurality of historical comprehensive scores are input as target data into the data classification model to obtain an electrolytic cell grade standard corresponding to the aluminum electrolytic cell, wherein the electrolytic cell grade standard includes score intervals corresponding to a plurality of electrolytic cell grades.

2. The method according to claim 1, characterized in that Determine the valid value range based on the data mean and data standard deviation corresponding to the target data in the following way: Calculating the data standard deviation according to a preset upper limit magnification and a preset lower limit magnification respectively, to obtain an upper limit value corresponding to the preset upper limit magnification and a lower limit value corresponding to the preset lower limit magnification; Calculating the data average value according to the upper limit value and the lower limit value respectively to obtain the maximum value of the valid interval corresponding to the upper limit value and the minimum value of the valid interval corresponding to the lower limit value; The valid numerical interval corresponding to the target data is determined according to the maximum value of the valid interval and the minimum value of the valid interval.

3. The method according to claim 1, characterized in that The target ratios corresponding to the data levels are determined according to the data ratios corresponding to the numerical sub-regions in the following manner: Performing quantitative statistics on the target data within the valid numerical interval to obtain the effective data volume, and performing quantitative statistics on the target data corresponding to each of the numerical sub-intervals to obtain the interval data volume corresponding to each of the numerical sub-intervals; Calculating the effective data amount according to the data amount of each interval respectively to obtain the data proportion corresponding to each data sub-interval respectively; If the ratio adjustment mode includes a recommended ratio mode, the data ratios corresponding to the respective value sub-intervals are displayed to the user, and the received user adjustment instructions are executed to obtain the target ratios corresponding to the respective data levels; If the ratio adjustment mode includes an expected ratio mode, the user expected ratios corresponding to each of the numerical sub-intervals are obtained, and calculations are performed based on the data ratio corresponding to the same data sub-interval and the user expected ratio to obtain the target ratios corresponding to each of the data levels.

4. The method according to claim 1, characterized in that Obtain historical data corresponding to multiple operating indicators of aluminum reduction cells, including: Obtaining an evaluation dimension corresponding to the aluminum electrolytic cell, wherein the evaluation dimension includes at least one of a production capacity dimension, an energy-saving dimension, an efficiency dimension, and a safety dimension; The operating indicators are set according to the evaluation dimension, and the data collection cycle corresponding to the aluminum electrolytic cell is set, wherein the operating indicators corresponding to the capacity dimension include current efficiency, the operating indicators corresponding to the energy-saving dimension include one or more of the set voltage, direct current power consumption, electrolyte temperature and noise level, the operating indicators corresponding to the efficiency dimension include aluminum level and / or molecular ratio, and the operating indicators corresponding to the safety dimension include electrolyte temperature and / or noise level; According to the operation indicators, the indicator data corresponding to the aluminum electrolytic cell in different data collection periods are collected; Determine abnormal data from the historical parameters corresponding to each of the operating indicators according to a preset abnormal ratio, and determine the climbing period data corresponding to the aluminum electrolytic cell in the climbing period from the historical parameters corresponding to each of the operating indicators; The abnormal data and the climbing period data are deleted from the indicator data to obtain historical data.

5. The method according to claim 1, characterized in that The historical data corresponding to the same operating indicator is input into the data classification model as the target data to obtain the indicator scoring criteria, including: Determine each of the operating indicators as a target indicator; In response to the target indicator, obtaining an assignment rule corresponding to the target indicator to generate a plurality of indicator evaluation scores corresponding to the target indicator according to the assignment rule, wherein the assignment rule is used to characterize the correlation between the historical data and the indicator evaluation score; The indicator evaluation score is used as the data level corresponding to the target indicator, the historical data corresponding to the target indicator is input into the data classification model as the target data, and the target numerical range corresponding to each indicator evaluation score is used as the indicator scoring standard corresponding to the target indicator.

6. The method according to claim 5, characterized in that According to the indicator scoring standards corresponding to each of the operating indicators, the historical data corresponding to the aluminum electrolysis cell is scored to obtain a historical comprehensive score corresponding to the aluminum electrolysis cell, including: Obtaining the indicator weights corresponding to the respective operating indicators; Determine the highest score of each indicator corresponding to each of the operating indicators from the indicator scoring criteria, and perform weighted calculation on the highest score of each indicator according to the weight of each indicator to obtain the total score of the indicator; According to the historical data corresponding to the aluminum reduction cell, the historical scores corresponding to the operating indicators are matched from the scoring standards of the indicators to obtain the historical scores, and the historical scores are weighted according to the weights of the indicators to obtain the historical total score; The total score of the indicators is calculated according to the historical total score to obtain the historical comprehensive score of the aluminum electrolytic cell.

7. The method according to any one of claims 1 to 6, characterized in that: After inputting a plurality of historical comprehensive scores as target data into the data classification model to obtain the electrolytic cell grade standard corresponding to the aluminum electrolytic cell, the method further includes: Obtaining current data corresponding to the aluminum electrolysis cell; Scoring the current data according to the indicator scoring standards corresponding to each of the operating indicators to obtain a current comprehensive score; The current comprehensive score is classified according to the electrolytic cell grade standard to determine the current grade corresponding to the aluminum electrolytic cell from each of the electrolytic cell grades.

8. The method according to claim 7, characterized in that The method further comprises: Displaying at least a portion of the current data, the current comprehensive score, and the current grade corresponding to the aluminum electrolytic cell as display content; The current data corresponding to the aluminum electrolysis cell is adjusted according to the display content to control the aluminum electrolysis cell to change from the current level to the target level, wherein the levels of the electrolysis cells are arranged in order from best to worst, and the target level is located before the current level.

9. A system for establishing a grade standard for electrolytic cells, characterized in that: include: An establishment module is used to establish a data classification model, wherein the data classification model is used to determine a valid numerical interval according to a data mean value and a data standard deviation corresponding to the target data, divide the valid numerical interval into a plurality of numerical sub-regions corresponding to the data levels, and determine a target ratio corresponding to each data level according to a data ratio corresponding to each numerical sub-region, so as to determine a target numerical interval corresponding to each data level from the valid numerical interval according to the target ratio; An acquisition module is used to obtain historical data corresponding to multiple operating indicators of the aluminum reduction cell; A first classification module is used to input the historical data corresponding to the same operating indicator as target data into the data classification model to obtain the indicator scoring standard, and score the historical data corresponding to the aluminum electrolytic cell according to the indicator scoring standard corresponding to each operating indicator to obtain the historical comprehensive score corresponding to the aluminum electrolytic cell; The second classification module is used to input multiple historical comprehensive scores as target data into the data classification model to obtain the electrolytic cell grade standard corresponding to the aluminum electrolytic cell, wherein the electrolytic cell grade standard includes score intervals corresponding to multiple electrolytic cell grades.

10. An electronic device, characterized in that: include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 8.

Images