CN114707970B

CN114707970B - Electrolytic production parameter determining method

Info

Publication number: CN114707970B
Application number: CN202210455865.1A
Authority: CN
Inventors: 林建平; 胡夏斌; 叶栋; 林建灶; 徐关峰; 胡双洋; 邵晶
Original assignee: Hangzhou Sanal Environmental Technology Co ltd
Current assignee: Hangzhou Sanal Environmental Technology Co ltd
Priority date: 2022-03-28
Filing date: 2022-03-28
Publication date: 2023-05-30
Anticipated expiration: 2042-03-28
Also published as: CN115271626A; CN116882906A; CN114707970A

Abstract

The embodiment of the specification provides an electrolytic production parameter determining method, which comprises the following steps: acquiring electrolytic production monitoring information; acquiring quality characteristics of the anode plate based on the electrolytic production monitoring information; and determining the electrolytic production parameters based on the difference between the quality characteristics and the standard characteristics of the anode plate.

Description

Electrolytic production parameter determining method

Description of the division

The present application is a divisional application filed in China with the application number 202210308708.8 and the name of "an electrolytic production improvement method and system", which is the application day 2022, month 03 and 28.

Technical Field

The specification relates to the field of electrolytic production, in particular to a method for determining electrolytic production parameters.

Background

In electrolytic production, some abnormal conditions (such as deformation of a die) sometimes occur in each process, and the abnormal conditions are often important influencing factors of the yield and the production efficiency of electrolytic production. The detection monitoring and adjustment improvement of the electrolytic production line are important means for ensuring the normal operation of the electrolytic production.

It is therefore desirable to provide an electrolytic production parameter determination method for improving electrolytic production efficiency.

Disclosure of Invention

One of the embodiments of the present specification provides an electrolytic production parameter determination method, the method including: acquiring electrolytic production monitoring information; the electrolytic production monitoring information at least comprises pouring detection information of the anode plate; acquiring quality characteristics of the anode plate based on the electrolytic production monitoring information; the quality features include at least composition information of the anode plate; determining an electrolysis production parameter based on the difference between the quality features and standard features of the anode plate; the standard features at least comprise component information of a standard anode plate; the electrolytic production parameters comprise at least one of electrolyte proportioning parameters and additive proportioning parameters.

One of the embodiments of the present specification provides an electrolytic production parameter determination system, comprising: the acquisition module is used for acquiring electrolytic production monitoring information; the electrolytic production monitoring information at least comprises pouring detection information of the anode plate; the first determining module is used for acquiring quality characteristics of the anode plate based on the electrolytic production monitoring information; the quality features include at least composition information of the anode plate; a second module for determining electrolysis production parameters based on the difference between the quality features and standard features of the anode plate; the standard features at least comprise component information of a standard anode plate; the electrolytic production parameters comprise at least one of electrolyte proportioning parameters and additive proportioning parameters.

One of the embodiments of the present specification provides an electrolytic production parameter determining apparatus, the apparatus comprising: at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement a method of electrolytic production parameter determination.

One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs an electrolytic production parameter determination method.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic illustration of an application scenario of an electrolytic production improvement system according to some embodiments of the present description;

FIG. 2 is a schematic block diagram of an electrolytic production improvement system according to some embodiments of the present disclosure;

FIG. 3 is an exemplary flow chart of an improved method of electrolytic production according to some embodiments of the present disclosure;

FIG. 4 is a schematic illustration of data acquisition of an electrolytic production improvement system according to some embodiments of the present disclosure;

FIG. 5 is an exemplary flow chart for analyzing electrolytic production monitoring information to determine electrolytic production improvement information according to some embodiments of the present disclosure;

FIG. 6 is an exemplary flow chart for improving an electrolytic production process based on electrolytic production improvement information according to some embodiments of the present disclosure;

FIG. 7 is a schematic illustration of determining adjustment information for an electrolytic production parameter based on a difference vector by an adjustment quantity validation model, according to some embodiments of the present disclosure;

FIG. 8 is a schematic illustration of determining adjustment information for an electrolytic production parameter based on a difference vector by a first adjustment quantity confirmation model and/or a second adjustment quantity confirmation model, according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram of determining adjustment information of an electrolytic production parameter based on a difference vector by an adjustment quantity confirmation model according to other embodiments of the present specification;

FIG. 10 is an exemplary flow chart of an anomaly determination model for determining adjustment information for an electrolytic production parameter shown in some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a schematic illustration of an application scenario of an electrolytic production improvement system according to some embodiments of the present description.

In some embodiments, the application scenario 100 may include a server 110, a network 120, one or more terminal devices 130, a storage device 140, a data acquisition apparatus 150, and an electrolysis production line 160. The application scenario 100 may monitor each process link (e.g., pouring, shaping, electrolysis, etc.) in the electrolytic production by implementing the method and/or process disclosed in the present specification, detect the anode plate and/or the cathode plate to obtain production monitoring information (e.g., anode plate pouring information, shaping process parameter information, etc.), determine electrolytic production improvement information (e.g., reject prediction information, reject disposal information, etc.) according to the production monitoring information, and further feed back to the production link and perform corresponding adjustment and improvement, thereby improving the efficiency of electrolytic production and the quality of electrolytic production.

The server 110 may be located at a site including, but not limited to, a control room of an electrolytic production line, an electrolytic production management center, and the like. In some embodiments, a collaboration platform is installed in server 110 that directs and coordinates the work of the electrolytic production personnel. Wherein, the staff can include electrolytic production operation staff, production safety management staff, electrolytic production comprehensive management staff, electrolytic technology expert and other staff related to electrolytic production operation and management. For example, the server 110 may obtain plate information, electrolytic production process monitoring information, electrolytic production process anomaly information, electrolytic production monitoring workflow, and the like.

The server 110 may communicate with the terminal device 130, the storage device 140, the data acquisition apparatus 150, and/or the electrolysis production line 160 to provide various functions of the application scenario 100. In some embodiments, the server 110 may receive relevant inspection information (e.g., plate composition information, casting process parameter information, etc.) after the inspection of the electrolytic production line 160 by the data acquisition device 150 from the terminal device 130 via, for example, the network 120. In other embodiments, server 110 may receive relevant electrolytic production process information (e.g., casting furnace temperature information, plate tapping time, etc.) in electrolytic production line 160 via, for example, network 120.

In some embodiments, the server 110 may be a single server or a group of servers. In some embodiments, server 110 may be connected locally to network 120 or remotely from network 120. In some embodiments, server 110 may be implemented on a cloud platform.

The network 120 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the application scenario 100 (e.g., the server 110, the network 120, etc.) may send information and/or data to another component in the application scenario 100 via the network 120.

In some embodiments, the user (e.g., electrolysis production personnel, technical specialists, etc.) may be the owner of the terminal device 130. The terminal device 130 may receive the user request and transmit information related to the request to the server 110 via the network 120. For example, the terminal device 130 may receive a request from a user to transmit the inspection information of the pad or the production process parameter information, and transmit information related to the request to the server 110 via the network 120. Terminal device 130 may also receive information from server 110 via network 120. For example, the terminal device 130 may receive production monitoring information from the server 110 regarding the data acquisition device 150 or the electrolysis production line 160. The determined one or more production monitoring information may be displayed on the terminal device 130. For another example, the server 110 may transmit production improvement information (e.g., mold replacement, electrolyte adjustment amount, etc.) or production abnormality information (e.g., electrolyte impurity content exceeding, mold abnormality, etc.) generated based on the detection information to the terminal device 130.

In some embodiments, the terminal device 130 may include a mobile device, a tablet computer, a laptop computer, an in-vehicle device, or the like, or any combination thereof. In some embodiments, the terminal device 130 may include a signal transmitter and a signal receiver configured to communicate with the data acquisition device 150 to acquire detection information of the sample to be tested. In some embodiments, terminal device 130 may be stationary and/or mobile. For example, the terminal device 130 may be directly installed on the server 110 and/or the electrolytic production device as part of the server 110 and/or the electrolytic production device. For another example, the terminal device 130 may be a mobile device, and the electrolysis production personnel may carry the terminal device 130 at a remote location relative to the server 110, the data acquisition device 150, and the electrolysis production line 160, and the terminal device 130 may be connected to and/or in communication with the server 110, the data acquisition device 150, and/or the electrolysis production line 160 via the network 120.

In some embodiments, the storage device 140 may be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., the server 110, the terminal device 130, the data acquisition apparatus 150). In some embodiments, the storage device 140 may be part of the server 110.

The storage device 140 may store data and/or instructions. The data may include data related to the user, the terminal device 130, the data acquisition means 150, etc. In some embodiments, the storage device 140 may store data acquired from the terminal device 130, the data acquisition apparatus 150, and/or the electrolysis line 160. In some embodiments, the storage device 140 may store data and/or instructions used by the server 110 to perform or use the exemplary methods described in this specification.

In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. In some embodiments, storage device 140 may be implemented on a cloud platform.

The data acquisition device 150 may acquire production monitoring information of the electrolytic production line, and further description of the detection information may be referred to fig. 4 and related description thereof, which will not be repeated herein.

In some embodiments, the data acquisition device 150 may be implemented by a variety of detection apparatus or means, and may include, for example, a manual detection module 150-1, a scan code detection module 150-2, an infrared detection module 150-3, an image acquisition module 150-4, a temperature detection module 150-5, an ultrasonic detection module 150-6, a sampling detection module 150-7, and a timing module 150-8, and further description of the manual detection module 150-1, the scan code detection module 150-2, the infrared detection module 150-3, the image acquisition module 150-4, the temperature detection module 150-5, the ultrasonic detection module 150-6, the sampling detection module 150-7, and the timing module 150-8 may be referred to in FIG. 4 and their related descriptions, and are not repeated herein.

The electrolysis line 160 refers to a system or apparatus for implementing an electrolysis process. The electrolytic production line 160 may include casting process 160-1, shaping process 160-2, plate arranging process, slot loading process, electrolytic process 160-3, slot discharging process, etc. In some embodiments, the electrolytic production line 160 may include electrolytic production equipment (e.g., casting furnaces, shaping milling machines, electrolytic cells, etc.), inspection equipment (e.g., inspection meters, inspection machines, inspection vehicles, inspection robots), positioning devices, and the like.

In some embodiments, electrolytic production equipment (e.g., casting furnaces, shaping milling machines, electrolytic cells, etc.) in the electrolytic production line 160 may be connected to the network 120 to communicate with one or more components of the application scenario 100 (e.g., server 110, terminal device 130, data acquisition device 150). For example, the electrolysis production line 160 may transmit production monitoring information (e.g., plate detection information, capacity information, etc.) to the server 110 via the network 120.

In some embodiments, server 110 may determine electrolytic production improvement information (e.g., electrolyte adjustment information, additive adjustment information, plate adjustment information, adjustment information for casting production parameters, casting mold deformation information, etc.) and/or electrolytic production anomaly information (e.g., plate appearance anomaly, casting furnace temperature anomaly, etc.) based on the production monitoring information (e.g., plate detection information, capacity information, etc.) and transmit it to electrolytic production line 160 via network 120. In some embodiments, the electrolytic production line 160 may feedback-modify corresponding parameters or process links in the electrolytic production based on the electrolytic production modification information. In some embodiments, the electrolytic production line 160 may prompt electrolytic production personnel according to electrolytic production anomaly information or anomaly handling schemes.

It should be noted that the application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of the present description. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario 100 may also include a database. However, such changes and modifications do not depart from the scope of the present specification.

FIG. 2 is a block schematic diagram of an electrolytic production improvement system 200 according to some embodiments of the present disclosure.

In some embodiments, as shown in fig. 2, the electrolytic production improvement system 200 may include an acquisition module 210, a determination module 220, and an improvement module 230. In some embodiments, the electrolytic production improvement system 200 may be used to subdivide into a plurality of specific sub-systems based on different functions, e.g., the electrolytic production improvement system 200 may include an electrolytic production parameter determination system, an anomaly monitoring of electrolytic production, and the like.

The acquisition module 210 may be used to acquire production monitoring information.

In some embodiments, the production monitoring information includes: at least one of pouring information and shaping information of the anode plate. Further, the pouring information of the anode plate comprises at least one of pouring procedure information and pouring detection information; the shaping information includes at least one of shaping process information and shaping detection information.

In some embodiments, the obtaining module 210 may be configured to obtain quality characteristics of the anode plate based on the electrolysis production monitoring information; the quality features include at least compositional information of the anode plate. In some embodiments, the anode plate is provided with a readable information carrier, and the acquisition module 210 may be configured to acquire electrolytic production monitoring information of the anode plate based on scanning the readable information carrier. For more description of determining to obtain production monitoring information, refer to fig. 3 and the related description thereof, and are not repeated here.

The determination module 220 may be configured to determine electrolytic production improvement information based on the production monitoring information.

In some embodiments, the electrolytic production improvement information includes at least one of improvement information of process parameters in the electrolytic production process, prediction information of reject, traceability information of production anomaly, disposal information of reject.

In some embodiments, the determination module 220 may be configured to determine the electrolysis production parameters based on a difference in quality characteristics of the anode plates from the standard characteristics; the standard features at least comprise component information of a standard anode plate; the electrolysis production parameters comprise at least one of electrolyte proportioning parameters and additive proportioning parameters. In some embodiments, the determination module 220 may be configured to determine adjustment information for the electrolysis production parameters based on differences in quality characteristics of the anode plates from the standard characteristics. Further, the determining module 220 may determine a difference vector of the quality feature and the standard feature; determining adjustment information of the electrolysis production parameters through an adjustment quantity confirmation model based on the difference vector; the adjustment information comprises electrolyte proportion adjustment amount and/or additive proportion adjustment amount.

In some embodiments, the determination module 220 may be configured to determine production anomaly information based on production monitoring information. Further, the determining module 220 may be configured to determine, based on the production monitoring information, feature information of the anode plate to be evaluated, the feature information including at least one of a component feature, a weight feature, and a casting mold; and predicting whether the anode plate to be evaluated is a qualified product or not based on the characteristic information of the anode plate to be evaluated. Further, the determining module 220 may obtain the similarity between the characteristic information of the current anode plate and the historical characteristic information of a plurality of historically produced unqualified anode plates by matching the two; taking the similarity as the risk degree of the unqualified products; and predicting whether the anode plate to be evaluated is a qualified product or not based on the risk degree.

In some embodiments, the determination module 220 may be used to determine process improvement information or an exception handling scheme based on production exception information. Further, the determination module 220 may be configured to determine an abnormal parameter of the anode plate based on the production monitoring information; and determining an abnormality type based on the abnormality determination model on the processing of the abnormality parameters of the anode plate, wherein the abnormality type comprises at least one of die abnormality, pouring parameter abnormality and mine source abnormality. For more explanation of determining the electrolytic production improvement information, reference is made to fig. 3 and the description thereof, and the details thereof will not be repeated here.

In some embodiments, the determining module 220 may include a first determining module (not shown) that may obtain quality characteristics of the anode plate based on the electrolysis production monitoring information; the quality features at least comprise component information of the anode plate, and electrolytic production parameters are determined based on the difference between the quality features and standard features of the anode plate; wherein the standard features at least comprise component information of a standard anode plate; the electrolysis production parameters comprise at least one of electrolyte proportioning parameters and additive proportioning parameters.

In some embodiments, the determination module 220 may include a second determination module (not shown) that may determine production anomaly information based on the production monitoring information and determine process improvement information or an anomaly handling scheme based on the production anomaly information.

The improvement module 230 may be used to improve the electrolytic production process based on the electrolytic production improvement information.

In some embodiments, the improvement module 230 may optimize the process based on the received electrolytic production improvement information. In some embodiments, the improvement module 230 may take emergency action in time based on the received abnormal condition of electrolytic production. For more explanation of the improved electrolytic production process, reference is made to fig. 3 and its associated description, which will not be repeated here.

In some embodiments, the electrolytic production improvement system 200 may further include a transmission module (not shown) that may be used to transmit electrolytic production anomaly and/or electrolytic production improvement information to a management control center or corresponding process. The transmission means may be wired transmission, for example, transmission through open wires, cables and optical cables, or wireless transmission, for example, transmission through microwaves, satellites, scattering, ultrashort waves, shortwaves, wi-Fi, bluetooth, infrared rays, etc.

It should be noted that the above description of the electrolytic production improvement system 200 and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the acquisition module 210, determination module 220, and improvement module 230 disclosed in fig. 1 may be different modules in a system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

FIG. 3 is an exemplary flow chart of an improved method of electrolytic production according to some embodiments of the present description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by the electrolytic production improvement system 200.

In step 310, production monitoring information is obtained. In some embodiments, step 310 may be performed by the acquisition module 210.

The production monitoring information may be information related to the production of anode plates. It is to be appreciated that anode plate production can include a variety of procedures (e.g., casting, shaping, etc.), and production monitoring information can include information related to at least one procedure of anode plate production.

In some embodiments, the production monitoring information may include at least one of casting information, shaping information of the anode plate.

The casting information may be information related to casting of the anode plate. It will be appreciated that the anode plate casting process may include heating and melting the raw materials used to form the anode plate to a liquid state, and then casting the liquid raw materials into an anode plate mold, and cooling and forming. For example, the casting information may include information about before casting (e.g., raw material composition, etc.), information about during casting (e.g., operating temperature of furnace, model number of mold, casting duration, etc.), information about after casting (e.g., anode plate size, etc.), etc.

In some embodiments, the casting information of the anode plate may include at least one of casting process information, casting detection information, wherein the casting process information may be information related to a process in a casting process, and the casting process information may include at least one of furnace information (e.g., parameters such as temperature, capacity, etc. of a furnace), casting formulation (e.g., component ratio of a mine source used for casting, etc.), casting mold information (e.g., number, size, service life, maintenance time, etc. of a casting mold), tapping time (e.g., time used for casting, tapping time, etc.), tapping weight (e.g., weight information of the anode plate after casting, residual residue weight, etc.).

The casting detection information may be information obtained by detecting the anode plate in the casting process, and may include at least one of component information (e.g., mineral source component, anode plate component, etc.), anode plate appearance information (e.g., thickness information, size information, flatness, perpendicularity information, etc.).

The shaping information may be information related to shaping of the anode plate. It can be appreciated that the anode plate shaping process may include shaping the cooling formation to make the anode plate smoother, thereby preventing the anode plate deformed subsequently from easily contacting the cathode plate, resulting in short circuit and reduced production efficiency. The shaping information may include related information before shaping (e.g., weight, size, etc. of the anode plate before shaping), related information in shaping (e.g., parameters of shaping equipment (e.g., ears, milling cutters, etc.), shaping duration, etc.), related information after shaping (e.g., weight, size, etc. of the anode plate after shaping), and the like.

In some embodiments, the shaping information may include at least one of shaping procedure information, shaping detection information, wherein the shaping procedure information may be information related to a shaping process, for example, the shaping procedure information may include a model and a conveying speed of a conveyor belt for conveying the anode plate to be shaped, a model and an operating parameter of a pressing ear (for example, a pressure generated by the pressing ear, etc.); at least one of the model number and the working parameters (such as the milling cutter rotating speed and the like) of the milling tool (such as the milling cutter and the like); the shaping detection information can be information obtained by detecting the anode plate in the shaping or after shaping, the shaping detection information can comprise at least one of weight information, thickness information, size information, flatness and perpendicularity information of the anode plate after shaping, the flatness of the anode plate can represent the concave-convex degree of the surface of the anode plate, and the perpendicularity information can represent the size of an included angle between the anode plate and the horizontal plane when the anode plate is vertically placed on the horizontal plane.

In some embodiments, the acquisition module 210 may acquire the production monitoring information via the data acquisition device 150. For further description of the data acquisition device 150, reference may be made to fig. 4 and its associated description, which is not repeated here.

At step 320, electrolytic production improvement information is determined based on the production monitoring information. In some embodiments, step 320 may be performed by determination module 220.

In some embodiments, the electrolysis production improvement information may be information related to the adjustment of the electrolysis related process. Among other things, the electrolysis-related processes may include anode plate production processes (e.g., casting, shaping, etc.), electrometallurgical processes, and the like. For example, the electrolytic production improvement information may include information on improvement of parameters of casting equipment (for example, an operating temperature of a furnace after improvement, a model number of a mold, a casting time period, etc.), information on improvement of parameters of a shaping equipment (for example, parameters of equipment such as an ear press, a milling cutter, etc.) after improvement, and the like.

In some embodiments, the electrolytic production improvement information may include at least one of improvement information of a process parameter in the electrolytic production process, prediction information of reject, traceability information of production abnormality, disposal information of reject.

The improved information of the process parameters in the electrolytic production process may be information related to the adjustment of the process parameters of the anode plate production and/or electrometallurgy. For example, the process parameters of anode plate production may include at least one of casting process parameters (e.g., source proportioning, flow and temperature of casting molten soup, equipment parameters, etc.), shaping equipment operating parameters (e.g., pressure of the press ears, rotational speed of the milling cutter, etc.), and the like.

In some embodiments, the improved information of the process parameters in the electrolytic production process may also include electrolytic production parameters. The electrolysis production parameter may be a technological parameter of electrometallurgical process, and the electrolysis production parameter may include at least one of electrolyte ratio, additive ratio, and the like. The electrolyte ratio may be a ratio of each component of the electrolyte (for example, ethylene carbonate, propylene carbonate, diethyl carbonate, dimethyl carbonate, methyl ethyl carbonate, lithium hexafluorophosphate, phosphorus pentafluoride, hydrofluoric acid, water, etc.), and the additive ratio may be a ratio of an additive (for example, thiourea, bone glue, abamectin, etc.) added to the electrolyte.

In some embodiments, the process parameter improvement information in the electrolytic production process may further include process improvement information, which may be information related to a process parameter for adjusting the production of the anode plate, for example, the process improvement information may include at least one of pouring process parameter (e.g., a mine source ratio, a flow rate and a temperature of pouring molten soup, an equipment parameter, etc.) improvement information, shaping equipment operation parameter (e.g., a pressure of a pressing ear, a rotational speed of a milling cutter, etc.), and the like.

The prediction information of the defective product may be information related to predicting whether the anode plate after casting and/or after shaping meets the quality requirement.

The retrospective information of production anomalies may be information related to equipment or process parameters associated with failed anode plates. For example, the traceability information of the production anomaly may include the number of furnaces and/or molds producing the failed anode plate, parameters at the time of producing the failed anode plate (e.g., the operating temperature of the furnace, the raw material composition, etc.).

The disposal information of the reject may be information related to disposal of the reject anode plate.

In some embodiments, the disposal information for the reject may include an exception handling scheme, which may be information related to the disposal of the reject anode plate. For example, the disposal information of the reject may be total re-production, total placement for use, partial re-production and partial placement for use, etc. In some embodiments, for reject products requiring partial retooling and partial placement for use, the ratio of the two treatments to the total reject product may be further determined, with reference to the description of the portion of fig. 5.

In some embodiments, the determination module 220 may determine the electrolytic production improvement information according to a variety of ways. For example, the determination module 220 may determine electrolytic production improvement information based on the production monitoring information from the historical data. Wherein the historical data may include production monitoring information for at least one historical point in time and its corresponding electrolytic production improvement information. It is understood that the determination module 220 may use the electrolytic production improvement information corresponding to the historical time points similar to the current production monitoring information as the current electrolytic production improvement information.

For another example, the determination module 220 may determine the electrolytic production improvement information based on the production monitoring information through human experience. Electrolytic production improvement information is determined, for example, by an industry expert based on the production monitoring information.

In some embodiments, the determination module 220 may analyze the electrolytic production improvement information to determine electrolytic production improvement information. For more description of the analysis of the electrolytic production improvement information, reference may be made to FIG. 5 and its associated description, which is not repeated here.

And 330, improving the electrolytic production process based on the electrolytic production improvement information. In some embodiments, step 330 may be performed by refinement module 230.

In some embodiments, the improvement module 230 may apply the electrolytic production improvement information to an electrolytic production process. For example, the improvement module 230 may set the electrolyte ratio, the additive ratio, etc. of the electrolytic cell according to the electrolytic production improvement information among the electrolytic production improvement information.

For more description of the improvement of the electrolytic production process based on the electrolytic production improvement information, reference may be made to fig. 6 and its related description, and the description is omitted here.

In some embodiments, by acquiring production monitoring information, determining electrolytic production improvement information based on the production monitoring information, and improving an electrolytic production process based on the electrolytic production improvement information, abnormal conditions can be found in time, and the electrolytic production process can be improved in time, so that normal operation of the electrolytic production process is ensured, and loss is reduced.

It should be noted that the above description of the process 300 is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description.

FIG. 4 is a schematic diagram of data acquisition for an electrolytic production improvement system according to some embodiments of the present description.

The electrolytic production improvement system may obtain production monitoring information 420 of the electrolytic production line 160 (e.g., process links such as casting process 160-1, shaping process 160-2, electrolytic process 160-3, etc.) through the data acquisition device 150.

The data acquisition device 150 is a unit module for the relevant data (e.g., production monitoring information) of the electrolytic production line 160. The relevant data may be quality characteristic information (e.g., appearance information, physical information, chemical information, etc.) of the anode plate and/or the cathode plate. The relevant data may also be electrolysis process parameter information (e.g., pouring process parameter information, shaping process parameter information, electrolysis process parameter information), and further description about quality feature information of the anode plate and electrolysis process parameter information may be referred to fig. 3 and related description thereof, and will not be repeated here.

In some embodiments, the data acquisition device 150 may acquire the production monitoring information based on at least one of manual, code scanning, infrared, image, ultrasonic, laser, sampling, and timing. For example, the data acquisition device 150 may include a manual detection module 150-1, a code scanning detection module 150-2, an infrared detection module 150-3, an image acquisition module 150-4, a temperature detection module 150-5, an ultrasonic detection module 150-6, a sampling detection module 150-7, and a timing module 150-8.

In some embodiments, the human detection module 150-1 may perform human detection observations and human input records of production monitoring information.

In some embodiments, the electrolytic production staff may manually observe and detect and input casting information (e.g., casting furnace information, casting source formulation, casting mold information, casting tapping time), shaping information (e.g., shaping equipment information), electrolytic information (e.g., in-tank time, electrolytic tank parameters), quality characteristics of the anode plate (component information of the anode plate, profile information of the anode plate, weight of the anode plate), source information (e.g., source information, source retention time), actual production anomaly information of the electrolytic process, yield information, etc.

In some embodiments, the sweep code detection module 150-2 may read readable information carriers (e.g., two-dimensional codes, bar codes, RFID tags, etc.) disposed on equipment and/or anode plates on the electrolysis production line to obtain production monitoring information.

In some embodiments, the electrolytic production device is provided with a readable information carrier, and the production monitoring information 420 associated with the electrolytic production device may be obtained based on scanning the readable information carrier. The scan code detection module 150-2 may perform a scan code operation. For example, a corresponding information carrier may be provided for each casting mold to record mold information, so that casting mold information (such as a mold identifier (e.g. a number), a manufacturer, and a service life) may be obtained directly through code scanning, and the code scanning detection module 150-2 may read a two-dimensional code provided on the casting mold to obtain casting mold information; for another example, a corresponding information carrier may be provided for each shaping device to record shaping device information, so that shaping device information (such as a shaping device model, a manufacturer, a service life, an overhaul interval, etc.) may be obtained directly through code scanning, and the code scanning detection module 150-2 may read a two-dimensional code set on the shaping device to obtain shaping device information; for another example, a corresponding information carrier may be provided for each cell for recording cell information, so that cell information (e.g., cell identification (e.g., number), manufacturer, service life, operational notice) may be obtained directly by scanning the code, and the scan code detection module 150-2 may read the two-dimensional code provided on the cell to obtain cell information.

In some embodiments, a readable information carrier may be disposed on the anode plate, the readable information carrier having an identification of the anode plate stored therein. Production monitoring information 420 for the anode plate corresponding to the identity of the anode plate may be obtained based on the scan-readable information carrier. In some embodiments, the scan code detection module 150-2 may perform a scan code operation.

In some embodiments, the system may perform the transfer and update of the anode plate information through the information carrier containing the identity of the anode plate, for example, corresponding operation information of the anode plate in the process may be written in the information carrier in each process of the electrolysis production line, specifically, pouring information may be written in the pouring process, shaping information may be written in the shaping process, electrolysis information may be written in the electrolysis process, and so on.

In some embodiments, specific identity information may be stored in a database, and an association correspondence is established between the identity information and the anode plate, for example, the anode plate and the information stored in the database are associated and corresponding by the identity of the anode plate, and if necessary, the specific information about the anode plate may be obtained by retrieving the corresponding information from the database based on the obtained identity.

In some embodiments, in the casting process 160-1, the system may create corresponding identity information for each anode plate obtained by casting and store the corresponding identity information in an information carrier readable by a scan code, and place the information carrier on the anode plate, based on the obtained mine source information and casting information (e.g., casting date, casting personnel, mold information, etc.) of the specific casting process.

In some embodiments, during the reforming process 160-2, the system may obtain corresponding anode plate information by scanning the information carrier on each anode plate and update the anode plate information based on the reforming information (e.g., reforming apparatus process parameters, anode plate weight information, etc.). In some embodiments, the shaped anode plate information may be updated to a database or information carrier.

In some embodiments, during electrolysis process 160-3, the system may obtain corresponding anode plate information by scanning an information carrier on the electrolysis cell and update the anode plate information based on electrolysis information (e.g., electrolysis equipment process parameters, anode plate profile information, etc.). In some embodiments, the shaped anode plate information may be updated to a database or information carrier.

In some embodiments, the infrared detection module 150-3 may be used to obtain temperature information of the anode plate and/or the cathode plate during various procedures. For example, the infrared detection module 150-3 (e.g., a thermal infrared imager, etc.) may perform infrared thermal imaging on the anode plate, receive infrared specific band signals of the thermal radiation of the anode plate, convert the signals into images and graphics that can be visually distinguished by human beings, and may further calculate the temperature value of the anode plate.

In some embodiments, the temperature information of the anode plate obtained in the casting procedure stage can be used for judging whether the casting furnace of the casting procedure works normally, whether the casting formula is abnormal or not, and the like. For example, when the anode plate temperature is higher than 1400 degrees, it may be judged that an abnormality may occur in the temperature setting of the casting furnace or that the impurity content in the casting recipe may exceed the standard.

In some embodiments, the infrared detection module 150-3 may perform infrared thermal imaging of the anode plate in each process and may determine quality feature information based on the thermally imaged image.

In some embodiments, a temperature detection module 150-5 (e.g., a temperature sensor, etc.) may be used to obtain temperature information for the casting furnace, the electrolyzer, the anode plate. For example, the temperature sensing module 150-5 may include a plurality of temperature sensors mounted at different locations within the casting furnace to obtain temperatures at different locations within the furnace; for another example, the temperature detection module 150-5 may include a plurality of temperature sensors mounted at different locations of the electrolytic cell to obtain temperatures of different locations of the electrolyte or anode plate; for another example, a temperature sensor mounted in the electrolyte may be used to obtain the electrolyte temperature, and for another example, a temperature sensor mounted on the surface of the anode plate may be used to obtain the anode plate temperature.

In some embodiments, excessive temperature differences (e.g., temperature differences greater than 1 ℃) of the electrolyte at different locations in the cell, or too low a temperature of the electrolyte, can cause crystallization near the cathode plate, thereby affecting the quality of the metal produced by subsequent electrolysis. The electrolyte temperature to be obtained may include at least two electrolyte temperatures measured at least two different depths of the electrolytic cell, wherein a difference between the two electrolyte temperatures measured at the two depths is a temperature difference between electrolyte layers.

In some embodiments, an image acquisition module 150-4 (e.g., camera, image sensor, etc.) may be used to acquire image information of the anode plate during various procedures. For example, the image acquisition module 150-4 may acquire image information of the anode plate and perform image recognition to acquire information of color, texture, shape, spatial relationship, etc. of the anode plate and/or the cathode plate of each process in the electrolytic production.

In some embodiments, the image information of the anode plate may be used to determine if the casting recipe is abnormal, if the casting mold is abnormal, if the shaping device is abnormal, etc. For example, if a corner defect is detected on a plurality of anode plates, it can be judged that the casting mold is likely to be abnormal.

Image recognition may include processing, analysis, and understanding to identify targets and objects in various different modes. For example, image acquisition may be performed by the image acquisition module 150-4; preprocessing the acquired image, eliminating irrelevant information in the image, recovering useful real information, enhancing the detectability of related information and simplifying data to the maximum extent; the preprocessed image is subjected to feature extraction, and color features, texture features, shape features and spatial relationship features of the anode plate and/or the cathode plate can be extracted based on algorithms such as a direction gradient Histogram (HOG) feature, a Local Binary Pattern (LBP) feature, a Haar-like feature and the like.

In some embodiments, ultrasonic detection module 150-6 (e.g., a pulse-reflex ultrasonic inspection apparatus) may be used to collect quality characteristic information (e.g., surface flatness, integrity, texture, thickness uniformity of the anode plate, etc.) of the surface of the anode plate during various procedures.

In some embodiments, a laser device (e.g., a laser thickness gauge) may also be used to collect quality characteristic information of the anode plate during each process. For example, the laser device may measure the thickness of the cathode plate and/or anode plate at different locations.

In some embodiments, quality characteristic information of the anode plate may be used to determine whether the casting mold is abnormal, whether the shaping apparatus is abnormal, and the like. For example, if a pit is detected on the surface of the anode plate, it can be judged that the casting die may be abnormal or that the extrusion part of the shaping device is abnormal. For another example, if the anode plate thickness is detected to be uneven, it may be determined that the parameter setting or the structure of the polishing component of the shaping device may be abnormal, or that the casting mold may be abnormal.

In some embodiments, the sample detection module 150-7 may be configured to randomly sample the anode plate during each process and analyze the chemical composition of the anode plate. For example, the sampling detection module 150-7 may randomly sample the anode plate, punch holes into the electrolytically generated metal attached to the cathode plate, and analyze the chemical composition of the sample; as another example, the sample detection module 150-7 may perform chemical component analysis on particulate samples of the surfaces of the cathode and/or anode plates, for example, to analyze the purity of the metal product (e.g., copper) and impurity conditions, including the type and content of the primary impurities (e.g., silver and its content).

In some embodiments, the chemical composition information of the anode plate may be used to determine whether the casting temperature, casting recipe proportion, or mine source is abnormal. For example, if the sulfur content in the anode plate is detected to be out of standard, it can be judged that the quality of copper in the mine source may be problematic. For another example, if the tin content of the anode plate is detected to exceed the standard, it can be judged that the temperature in the pouring furnace may not reach a reasonable temperature range, and the oxidation is incomplete.

In some embodiments, the manual detection module 150-1, the code scanning detection module 150-2, the infrared detection module 150-3, the image acquisition module 150-4, the temperature detection module 150-5, the ultrasonic detection module 150-6, the sampling detection module 150-7, and the timing module 150-8 described above may also implement other functions. For example, the infrared detection module 150-3 may also be used to obtain positional information of the anode plate. For example, the infrared detection module 150-3 may collect an infrared thermal image of the electrolytic cell through a thermal imager, process the infrared thermal image to obtain a pixel point of the anode plate with abnormal temperature, and finally determine a corresponding anode plate position according to the pixel point.

In some embodiments, the data acquisition device 150 may acquire composition information of a number of anode plates to be evaluated; and processing the component information based on the risk prediction model to obtain a risk prediction value of the anode plate to be evaluated as abnormal. Further, the data obtaining device 150 may obtain the production monitoring information of the anode plate with the risk prediction value greater than the preset threshold based on the risk prediction value, and further obtain the production monitoring information of the anode plate with the greater risk prediction value. See step 310 for a description of obtaining production monitoring information.

In some embodiments, the composition information of several anode plates to be evaluated can be obtained by a probe. For example, the data acquisition device 150 may acquire composition information of the anode plate to be evaluated using a metal composition analyzer.

In some embodiments, the risk prediction value that the anode plate to be evaluated is abnormal may be obtained by performing analysis processing based on the input component information of the plurality of anode plates to be evaluated through a risk prediction model.

In some embodiments, the input data for the risk prediction model is constituent information for a plurality of anode plates. The output data of the risk prediction model is a risk prediction value of the anode plate to be evaluated being abnormal.

In some embodiments, the risk prediction model may be a convolutional machine learning model (CNN), a complete convolutional neural network (FCN) model, a generative resistant network (GAN), a Back Propagation (BP) machine learning model, a Radial Basis Function (RBF) machine learning model, a Deep Belief Network (DBN), an Elman machine learning model, or the like, or a combination thereof.

In some embodiments, the risk prediction model may be a Graph Neural Network (GNN) model. The nodes of the GNN model are a plurality of anode plates, the edges are connecting lines between two nodes, and the two nodes poured by the same die or shaped by the same device are connected. The node characteristics may include compositional information of the anode plate and the edge characteristics may include information (e.g., die number) of the casting die or the shaping device.

In some embodiments, parameters of the risk prediction model may be trained by a plurality of labeled training samples. In some embodiments, multiple sets of training samples may be obtained, where each set of training samples may include multiple training data and labels corresponding to the training data, where the training data may include component information of a plurality of historically produced anode plates, and the labels of the training data may be used to manually label whether the anode plates corresponding to the component information are normal, such as normal or abnormal, according to the component information.

Parameters of the initial risk prediction model can be updated through a plurality of groups of training samples, and a trained risk prediction model is obtained.

In some embodiments, parameters of the initial risk prediction model may be iteratively updated based on a plurality of training samples such that a loss function of the model satisfies a preset condition. For example, the loss function converges, or the loss function value is smaller than a preset value. And when the loss function meets the preset condition, model training is completed, and a trained risk prediction model is obtained.

In some embodiments, when the risk prediction value of one or more anode plates is greater than a preset threshold, the data acquisition device 150 may acquire production monitoring information of the corresponding one or more anode plates. Wherein the preset threshold value can be set by electrolytic production staff (such as expert, technician, etc.) depending on their past experience.

According to the method disclosed by the embodiments of the specification, the risk prediction is carried out on the anode plates by utilizing the data relationship among the anode plates produced by the same pouring die or shaping equipment, the information acquisition objects can be better screened in advance, the high-risk objects are determined, the further information acquisition is carried out on the high-risk objects, the effectiveness of the acquisition of the electrolytic production monitoring data is improved, the acquisition efficiency of the electrolytic production monitoring data is improved, and the data processing capacity is reduced.

FIG. 5 is an exemplary flow chart for analyzing electrolytic production monitoring information 510 to determine electrolytic production improvement information 520, according to some embodiments of the present disclosure.

Referring to FIG. 5, in some embodiments, the determination module 220 may analyze the electrolytic production monitoring information 510 to determine electrolytic production improvement information 520.

In some embodiments, the electrolytic production monitoring information 510 may include casting detection information 510-1 of the anode plate, the electrolytic production improvement information 520 may include electrolytic production parameters 520-1, the determination module 220 may obtain quality characteristics of the anode plate based on the electrolytic production monitoring information, and determine the electrolytic production parameters 520-1 based on a difference between the quality characteristics and standard characteristics of the anode plate, wherein the electrolytic production parameters 520-1 include at least one of electrolyte formulation parameters and additive formulation parameters.

It can be appreciated that the electrolyte proportioning parameters can be adjusted according to the component information of the anode plate, for example, the content of copper component in the anode plate is higher, so that the concentration of copper ions in the electrolyte is too high in the electrolysis process, and the quality of copper precipitated from the cathode plate is further reduced (for example, the surface of copper precipitated from the cathode plate is roughened, etc.), therefore, water is required to be added for dilution (i.e. the proportion of water in the electrolyte is increased); conversely, the lower copper content in the anode plate results in too small a concentration of copper ions in the electrolyte during electrolysis, and further reduces the quality of copper precipitated from the cathode plate (e.g., loosening copper precipitated from the cathode plate, easily growing particles on the surface, etc.), and thus, it is necessary to increase the copper ion concentration (e.g., increase the proportion of copper sulfate (CuSO 4) solution, etc.).

The proportioning parameters of the additives can be adjusted according to the component information of the anode plate, for example, the silver component content of the anode plate is higher than a certain threshold value, the concentration of anions is possibly larger, and a proper amount of hydrochloric acid can be added to generate AgCl, so that silver ions are precipitated into anode mud, and the loss of noble metals is reduced. For more description of the pouring detection information, electrolyte formulation parameters, and additive formulation parameters, reference may be made to fig. 3 and related description thereof, and no further description is given here.

The quality characteristics of the anode plate may be information related to the anode plate currently cast, for example, composition information of the anode plate. In some embodiments, the determination module 220 may determine quality characteristics of the anode plate based on the placement detection information.

The standard characteristic may be a quality characteristic of a standard anode plate, for example, compositional information of the standard anode plate. The standard anode plate may be an anode plate that meets a preset standard in historical casting production, for example, an anode plate that meets the preset standard in size, weight, composition, etc. in historical casting production. In some embodiments, the determination module 220 may obtain the standard characteristics from the storage device 140, the data acquisition apparatus 150, or an external data source. In some embodiments, the determination module 220 may detect the anode plate by the data acquisition device 150 to acquire the standard features.

In some embodiments, the determination module 220 may determine at least one of an electrolyte formulation parameter, an additive formulation parameter based on a difference in the composition information of the anode plate and the composition information of the standard anode plate.

In some embodiments, the determination module 220 may determine at least one of the electrolyte formulation parameters, the additive formulation parameters based on the difference in the composition information of the anode plate and the composition information of the standard anode plate in a variety of ways. For example, the determination module 220 may determine the electrolysis production parameter 520-1 based on the difference in the composition information of the anode plate and the composition information of the standard anode plate based on historical data. Wherein, the historical data may include differences in composition information of the anode plate and composition information of the standard anode plate at least one historical point in time and their corresponding electrolysis production parameters 520-1.

Illustratively, the copper component content of the anode plate cast at the historical time point a is 1% more than that of the standard anode plate, the copper component content of the anode plate cast at the historical time point B is 3% more than that of the standard anode plate, the copper component content of the anode plate cast at the historical time point C is 1% less than that of the standard anode plate, and the difference between the component information of the anode plate cast at present and the component information of the standard anode plate is: the copper component content in the anode plate generated by casting is 0.5% more than that in the standard anode plate, and the determining module 220 may select, according to the difference value, the electrolyte ratio parameter and the additive ratio parameter corresponding to the anode plate generated by casting at the historical time point a with the closest difference value as the electrolyte ratio parameter and the additive ratio parameter of the anode plate generated by casting at present.

In some embodiments, the determination module 220 may determine at least one of the electrolyte formulation parameters, the additive formulation parameters based on the constituent information of the anode plate through human experience. For example, at least one of electrolyte formulation parameters and additive formulation parameters is determined by industry experts based on the composition information of the anode plate.

In some embodiments, the determination module 220 may determine adjustment information for the electrolysis production parameter based on the difference between the composition information for the anode plate and the composition information for the standard anode plate, and determine the electrolysis production parameter 520-1 based on the adjustment information.

It will be appreciated that the standard features correspond to standard electrolysis production parameters, which may include at least one of standard electrolyte formulation parameters, standard additive formulation parameters. The adjustment information of the electrolysis production parameter may be information related to adjustment of a standard electrolysis production parameter (e.g., standard electrolyte formulation parameter, standard additive formulation parameter, etc.) based on the quality characteristics of the anode plate, for example, at least one of a standard electrolyte formulation adjustment amount, a standard additive formulation adjustment amount, etc.

In some embodiments, the determination module 220 may determine the adjustment information for the electrolysis production parameters based on the difference in composition information for the anode plate and composition information for the standard anode plate through human experience. For example, adjustment information of the electrolytic production parameters is determined by industry experts based on the difference of the composition information of the anode plate and the composition information of the standard anode plate.

In some embodiments, the determination module 220 may determine the adjustment information based on a difference in the composition information of the anode plate and the composition information of the standard anode plate from the historical data. The historical data may include differences between the composition information of the anode plate and the composition information of the standard anode plate at least at one historical time point and corresponding adjustment information. Taking the above-mentioned historical time point A, B, C as an example, the determining module 220 may select, according to the difference value, adjustment information corresponding to the anode plate cast at the historical time point a closest to the difference value as adjustment information of the anode plate cast currently.

In some embodiments, the determination module 220 may directly determine adjustment information based on a fitted curve, where the fitted curve may characterize the condition of the adjustment information of the electrolysis production parameters (e.g., electrolyte formulation adjustment and/or additive formulation adjustment, etc.) as a function of the difference in quality characteristics from the standard characteristics.

In some embodiments, the determining module 220 may obtain, based on the historical data, a correspondence between the difference between the quality feature and the standard feature and the adjustment information of the electrolysis production parameter through multiple linear regression fitting. For example, the determination module 220 may establish a correspondence between the difference of the quality feature and the standard feature and the adjustment information of the electrolytic production parameter based on a set of multiple linear regression equations, wherein the independent variables of the set of multiple linear regression equations may include the difference of the quality feature and the standard feature, and the dependent variables of the set of multiple linear regression equations may include the adjustment information of the electrolytic production parameter (e.g., the electrolyte ratio adjustment amount and/or the additive ratio adjustment amount, etc.).

In some embodiments, the determining module 220 may substitute the difference between the quality feature and the standard feature of at least one historical time point into an independent variable of the multiple linear regression equation set, substitute the adjustment information of the corresponding electrolysis production parameter into the dependent variable of the multiple linear regression equation set, solve the multiple linear regression equation set based on a least square method or the like, obtain a corresponding relationship between the difference between the quality feature and the standard feature and the adjustment information of the electrolysis production parameter, and generate a fitting curve based on the corresponding relationship.

It can be understood that, when the adjustment information of the current electrolytic production parameter needs to be determined, the determining module 220 may substitute the difference between the quality feature and the standard feature determined this time into the independent variable of the multiple linear regression equation set, so as to obtain the adjustment information of the current electrolytic production parameter.

In some embodiments, the correspondence between the difference of the quality features and the standard features and the adjustment information of the electrolysis production parameters is fitted by multiple linear regression, so that the adjustment information of the electrolysis production parameters determined based on the difference of the quality features and the standard features is more accurate.

In some embodiments, the determining module 220 may determine the adjustment information through an adjustment quantity confirmation model, and further description of the adjustment quantity confirmation model may be referred to in fig. 7 and related description thereof, which are not repeated herein.

In some embodiments, the determination module 220 may adjust standard electrolytic production parameters (e.g., standard electrolyte formulation parameters, standard additive formulation parameters, etc.) based on the adjustment information of the electrolytic production parameters.

For example, if the copper content of the anode plate is greater than that of the standard anode plate and the difference is 4%, the adjustment information of the electrolytic production parameters includes: the thiourea ratio was increased from the standard ratio (e.g., 10 mg/L) to 15mg/L so that thiourea could sufficiently react with copper ions in the anode plate to form a complex, increasing the smoothness of the copper deposition surface. For another example, if the silver content of the anode plate is greater than that of the standard anode plate and the difference is 2%, the adjustment information of the electrolytic production parameters includes: the hydrochloric acid ratio is increased from the standard ratio (for example, 1%) to 2%, and the added hydrochloric acid is added in a proper amount, so that silver ions in the anode plate are completely precipitated into anode mud, and the loss of noble metals is reduced.

In some embodiments, the electrolytic production monitoring information 510 may include at least one of casting information 510-2, shaping information 510-3 of the anode plate, the electrolytic production improvement information 520 may include process improvement information 520-2 or an anomaly handling scheme 520-3, and the determination module 220 may determine production anomaly information based on the production monitoring information and determine the process improvement information 520-2 or the anomaly handling scheme 520-3 based on the production anomaly information. For further description of the casting information 510-2, shaping information 510-3, process improvement information 520-2, and exception handling scheme 520-3 for the anode plate, see fig. 3 and its associated description, which are not repeated here.

The production anomaly information may be information related to a process of producing a failed anode plate. For example, the production anomaly information may fail information associated with the casting process and/or the shaping process of the anode plate.

In some embodiments, the determination module 220 may determine a failed anode plate based on the production monitoring information, obtain characteristic information of the failed anode plate, and determine equipment anomaly information in the casting process and/or equipment anomaly information in the shaping process based on the characteristic information of the failed anode plate.

In some embodiments, the determination module 220 may determine the failed anode plates based on the production monitoring information through human experience. For example, a failed anode plate is determined by an industry expert based on the production monitoring information.

The characteristic information of the failed anode plate may be information related to the failed anode plate. For example, the characteristic information of the failed anode plate may include weight information, shape information, composition information, and the like of the failed anode plate.

The equipment anomaly information in the casting process may be information related to anomaly equipment used in the failed anode plate casting process. For example, the equipment abnormality information in the casting process may include abnormal parameters of the casting furnace, the casting mold, and the like.

In some embodiments, the determining module 220 may determine equipment anomaly information in the casting process from the production monitoring information based on the characteristic information of the failed anode plate, e.g., profile information of the failed anode plate includes: the surface irregularities, the equipment abnormality information in the casting process may include abnormal parameters of a furnace producing the failed anode plate in the production monitoring information, for example, abnormal temperature of the furnace during casting the failed anode plate, and the like.

The equipment anomaly information in the shaping process may be information related to anomaly equipment used in the rejected anode plate shaping process. For example, the equipment anomaly information in the truing process may include conveyor belts, press lugs, anomaly parameters of a grinding tool (e.g., milling cutter), and the like.

In some embodiments, the determining module 220 may determine the equipment anomaly information in the shaping process from the production monitoring information based on the characteristic information of the failed anode plate, for example, when the characteristic information of the failed anode plate includes weight information, shape information of the failed anode plate, the determining module 220 may determine the equipment anomaly information in the shaping process from the production monitoring information may include: model and working parameters (such as pressure generated by pressing the lug) of the unqualified anode plate; and (3) the model and working parameters (such as the rotating speed of the milling cutter and the like) of a polishing tool (such as the milling cutter) for polishing the unqualified anode plate.

In some embodiments, the determination module 220 may determine feature information of the anode plate to be evaluated based on the production monitoring information, the feature information including at least one of a constituent feature, a weight feature, and a casting mold.

In some embodiments, the determination module 220 may predict whether the anode plate to be evaluated is a good based on at least one of the component characteristics, the weight characteristics, the casting mold through human experience. For example, whether the anode plate to be evaluated is a good is predicted by an industry expert based on whether the anode plate to be evaluated is a good.

In some embodiments, the determining module 220 may predict whether the anode plate to be evaluated is a good based on at least one of the component characteristics, the weight characteristics, the casting mold by preset evaluation rules. The preset evaluation rule can represent the standard related to the component characteristics, the weight characteristics and the pouring die and used for judging whether the anode plate is a qualified product or not. For example, the preset evaluation rules may include: the weight is between 2kg and 3kg, and if the weight of the anode plate to be evaluated is less than 2kg, the determining module 220 may determine that the anode plate to be evaluated is not a qualified product.

In some embodiments, the determination module 220 may predict whether the anode plate to be evaluated is a good based on the characteristic information of the anode plate to be evaluated based on the historical characteristic information of the plurality of historically produced failed anode plates, which may include at least one of constituent characteristics, weight characteristics, and casting molds of the historically produced failed anode plates. For example, the determining module 220 may obtain the similarity between the feature information of the current anode plate (i.e. the anode plate to be evaluated) and the historical feature information of a plurality of historically produced unqualified anode plates, and use the similarity as the risk of being a unqualified product; and predicting whether the anode plate to be evaluated is a qualified product or not based on the risk degree.

In some embodiments, the determining module 220 may obtain the similarity between the feature information of the current anode plate and the historical feature information of the historically produced failed anode plate through a similarity algorithm, where the similarity algorithm may include at least one of euclidean metric and cosine distance. For example, the determining module 220 may convert the characteristic information of the current anode plate into a first vector, convert the historical characteristic information of a plurality of historically produced failed anode plates into a plurality of second vectors, and determine the similarity between the first vector and each of the second vectors based on the cosine distance.

It may be appreciated that the determining module 220 may determine the risk of the reject of the current anode plate based on the feature information of the current anode plate and the similarity of the historical feature information of each historically produced reject anode plate. For example, the determining module 220 may take the average of the feature information of the current anode plate and the similarity of the historical feature information of each historically produced failed anode plate as the risk of the failed anode plate. In some embodiments, the anode plate to be evaluated having a risk of greater than a preset risk threshold is a reject.

In some embodiments, the determining module 220 may obtain the feature information of the anode plate whose prediction result is failed, predict at least one of equipment die anomaly information, ore blending anomaly information, and prediction information of the same batch of rejects based on the feature information of the failed anode plate, where the same batch of rejects may be anode plates produced using the same ore blending, casting equipment (e.g., furnace, die, etc.) and/or shaping equipment (e.g., pressing ears, milling cutters, etc.) as the failed anode plate. For example, when the antimony impurity content of a failed anode plate is high, the determination module 220 may predict that there is an abnormality in ore proportioning and may predict that anode plates produced in the same lot as the failed anode plate are all failed. For another example, when the thickness of a failed anode plate is abnormal, the determination module 220 may predict that the mold type is abnormal and may predict that anode plates produced in the same lot as the failed anode plate are all failed.

In some embodiments, the determination module 220 may predict at least one of equipment die anomaly information, mine allocation anomaly information, and same lot reject prediction information based on the characteristic information of the rejected anode plates through human experience.

In some embodiments, the determination module 220 may predict at least one of equipment die anomaly information, mine-making anomaly information, and same-lot reject prediction information based on the characteristic information of the rejected anode plates via a machine learning model.

In some embodiments, the determination module 220 may predict the probability of device die anomaly based on the characteristic information of the failed anode plate through a first prediction model, the input of the first prediction model may be the characteristic information of the failed anode plate, and the output of the first prediction model may be the probability of device die anomaly.

In some embodiments, the determining module 220 may predict the probability of equipment die anomaly based on the characteristic information of the failed anode plate through a second prediction model, the input of which may be the characteristic information of the failed anode plate, and the output of which may be the probability of mine allocation anomaly.

In some embodiments, the determining module 220 may predict the device die anomaly information based on the feature information of the failed anode plate through a third prediction model, the input of the third prediction model may be the feature information of the failed anode plate, and the output of the third prediction model may be the probability that the anode plates of the same lot are failed.

In some embodiments, the determining module 220 may train the initial first prediction model, the initial second prediction model, and/or the initial third prediction model through a plurality of labeled training samples, where one training sample corresponds to one historically produced failed anode plate, the training sample may include characteristic information of the historically produced anode plate, and the label of the training sample may include anomaly information corresponding to the historically produced anode plate (e.g., whether the equipment mold is abnormal, whether the ore distribution is abnormal, whether the same batch production is a failed product).

In some embodiments, the determining module 220 may train the initial first prediction model, the initial second prediction model, and/or the initial third prediction model multiple times in a common manner (e.g., gradient descent, etc.) until the trained initial first prediction model, the trained initial second prediction model, and/or the trained initial third prediction model meet a preset condition, take the trained initial first prediction model as a first prediction model for predicting equipment mold anomaly information, take the trained initial second prediction model as a second prediction model for predicting mine allocation anomaly information, and/or take the trained initial third prediction model as a third prediction model for predicting the same lot of reject prediction information. The preset condition may be that the loss function of the updated initial first prediction model, the updated initial second prediction model, and/or the updated initial third prediction model is smaller than a threshold, converges, or the number of training iterations reaches a threshold.

In some embodiments, the first, second, and/or third prediction models may also be pre-trained by the server 110 or a third party and stored in the storage device 140, and the determination module 220 may directly invoke the first, second, and/or third prediction models from the storage device 140.

In some embodiments, the first, second, and third prediction models may include, but are not limited to, neural Networks (NNs), decision Trees (DTs), linear regression (Linear Regression, LR), and the like, in combination with one or more of them.

In some embodiments, the determination module 220 may determine the process improvement information 520-2 or the exception handling scheme 520-3 based on the production exception information.

In some embodiments, the determination module 220 may determine the process improvement information 520-2 or the exception handling scheme 520-3 based on the production exception information through human experience. For example, process improvement information 520-2 or anomaly handling schemes 520-3 are determined by industry professionals based on the production anomaly information.

In some embodiments, the determination module 220 may determine the process improvement information 520-2 or the exception handling scheme 520-3 based on the production exception information based on a preset adjustment rule table, wherein the preset adjustment rule table may be used to record a preset adjustment rule that may characterize a correspondence of the production exception information to the process improvement information 520-2 or the exception handling scheme 520-3.

For example, the preset adjustment rule may include: when the related equipment is abnormal, suspending the related equipment and overhauling the related equipment; if the anode plate is unqualified, the anode plate produced in the same batch with the unqualified anode plate is reworked, and the like. It is appreciated that the determination module 220 may look up the process improvement information 520-2 or the exception handling scheme 520-3 from a preset adjustment rule table based on the production anomaly information. Illustratively, the determination module 220 obtains keywords based on the production anomaly information and looks up the process improvement information 520-2 or the anomaly handling device 520-3 from a preset adjustment rule table based on the keywords.

In some embodiments, the determining module 220 may determine the abnormality type through the processing of the abnormality parameters of the anode plate by the abnormality determination model, and further description of the abnormality determination model may be referred to in fig. 10 and related description thereof, which is not repeated herein.

In some embodiments, the determining module 220 may obtain process parameter adjustment information through processing the production monitoring information by the third model, where the process parameter adjustment information includes a casting process adjustment parameter or a shaping process adjustment parameter, where the casting process adjustment parameter may be a parameter that adjusts a casting process of producing the failed anode plate, for example, an adjustment parameter of a furnace temperature, a model adjustment parameter of a mold, a casting recipe adjustment parameter, and the like; the shaping process adjustment parameters may be parameters that adjust shaping procedures for producing a defective anode plate, such as pressure adjustment parameters of a press lug, rotational speed adjustment parameters of a milling cutter, and the like.

The third model may be a machine learning model for determining process parameter adjustment information. The input of the third model may produce monitoring information and the output of the third model may be process parameter adjustment information.

In some embodiments, the determining module 220 may train the initial third model through a plurality of labeled training samples, where one training sample corresponds to one historically produced anode plate, the training sample may include production monitoring information of the historically produced anode plate, and the label of the training sample may include process parameter adjustment information corresponding to the historically produced anode plate.

In some embodiments, the determining module 220 may train the initial third model multiple times in a common manner (e.g., gradient descent, etc.) until the trained initial third model meets a preset condition, and use the trained initial third model as the third model for predicting the process parameter adjustment information. The preset condition may be that the loss function of the updated initial third model is smaller than a threshold, converges, or the number of training iterations reaches the threshold.

In some embodiments, the third model may also be pre-trained by the server 110 or a third party and then stored in the storage device 140, and the determination module 220 may invoke the third model directly from the storage device 140.

In some embodiments, the third model may include, but is not limited to, neural Networks (NNs), decision Trees (DTs), linear regression (Linear Regression, LR), and the like, combinations of one or more thereof, and the like.

In some embodiments, the process parameter adjustment information may be obtained quickly and accurately by processing the production monitoring information by the third model.

In some embodiments, the determination module 220 may obtain feature information of the anode plate whose prediction result is failed, process the feature information of the failed anode plate based on a fourth model, predict a treatment cost of different treatment modes, wherein the treatment modes include at least one of standing-by and retooling. In some embodiments, the determination module 220 may detect a failed anode plate via the data acquisition device 150 to obtain characteristic information of the failed anode plate, e.g., the determination module 220 may obtain profile information of the failed anode plate via the ultrasonic detection module 150-6.

The fourth model may be a machine learning model for determining process parameter adjustment information. The input of the fourth model may be characteristic information of the failed anode plate, and the output of the fourth model may be treatment cost of different treatment modes.

The training and model structure of the fourth model is similar to the third model, and for further description of the training and model structure of the fourth model, reference may be made to the description of the third model, it being understood that the training samples for training the fourth model include characteristic information of historically produced failed anode plates, the training samples may include characteristic information of historically produced failed anode plates, and the labels of the training samples may include treatment costs for different treatment modes.

In some embodiments, the determination module 220 may determine a manner of disposing of the reject based on the predicted disposal cost. For example, the determination module 220 may treat the treatment mode corresponding to the smallest predicted treatment cost as the treatment mode of the reject.

In some embodiments, processing the feature information of the failed anode plate based on the fourth model may quickly predict the disposal costs of different disposal modes in order to determine the appropriate disposal mode of the failed article while reducing the cost investment in disposing the failed article.

FIG. 6 is an exemplary flow chart for improving an electrolytic production process based on electrolytic production improvement information according to some embodiments of the present description.

As shown in FIG. 6, in some embodiments, the improvement module 230 may improve the electrolytic production process 620 based on the electrolytic production improvement information 610. For example, the improvement module 230 may obtain at least one of improvement information of a process parameter, prediction information of a defective product, traceability information of a production abnormality, and disposal information of a defective product in the electrolytic production process, and improve at least one of an anode plate pouring process, a shaping process, disposal of a defective product, and an electrometallurgical process based on the above information.

In some embodiments, the improvement module 230 may improve the casting and/or shaping process of the anode plate based on the improvement information of the process parameters and/or the traceability information of the production anomalies in the electrolytic production process.

For example, the modification module 230 may set the temperature of the furnace, etc., according to the operating temperature of the furnace after modification. For another example, the improvement module 230 may replace the mine source based on the traceability information of the production anomaly, manually destroy the anomaly mine source raw material according to the mine source proportioning, evaluate and rank the suppliers according to the qualification rate of the mine source production supplied by the mine source suppliers, and prioritize the mine source suppliers with higher qualification rate of the mine source production. For another example, the improvement module 230 may generate automatic cues for periodic inspection, repair, replacement of casting molds for producing failed anode plates based on traceability information of production anomalies. For another example, the improvement module 230 may generate alarm prompts (including text, voice, flashing of alarm lamps, buzzing, etc.) of different levels based on the traceability information of the production anomaly, and the improvement module 230 may switch the shaping mode from extrusion to polishing according to the improvement information of the process parameters in the electrolytic production process.

In some embodiments, the improvement module 230 may adjust the process parameters of the electrometallurgical process based on the improvement information of the process parameters in the electrolytic production process. For example, the improvement module 230 may control the associated equipment to formulate an electrolyte according to the electrolysis production parameters, and may also control the associated equipment to add at least one additive to the formulated electrolyte.

In some embodiments, the improvement module 230 may process the anode plates that are predicted to be failed based on the prediction information of the failed good. For example, improvement module 230 may notify the relevant staff personnel to manually detect the predicted reject based on the predicted information of the reject.

In some embodiments, the improvement module 230 may complete the disposal of the reject based on the disposal information of the reject. For example, the improvement module 230 may control related equipment or personnel to stand-by or re-oven the rejected anode plates based on the disposal information of the rejected product.

According to the method disclosed by some embodiments of the specification, the generation of unqualified products can be effectively reduced by improving the electrolytic production process based on the electrolytic production improvement information, and the electrolytic production process is ensured to be effectively carried out, for example, pouring and/or shaping processes of the anode plate are improved by the improvement information of process parameters and/or the traceability information of production abnormality in the electrolytic production process, so that the anode plate produced subsequently meets the requirements; for another example, the technological parameters of electrometallurgy are adjusted through the improved information of the technological parameters in the electrolytic production process, so that the quality of the metal precipitated by electrometallurgy is improved; for another example, by processing the anode plates that are predicted to be failed based on the prediction information of the failed, the amount of anode plates that the operator needs to detect can be reduced to quickly determine other failed anode plates; for example, the disposal of the defective products is completed based on the disposal information of the defective products, so that the defective anode plates are prevented from entering the electrometallurgical process, further waste of electrolytic raw materials (e.g., electrolytic tanks, electrolytic solutions, etc.) is caused, and further cost waste caused by the defective products is reduced to the maximum extent.

As shown in fig. 7, in some embodiments, the determination module 220 may determine a difference vector 710 of the quality feature and the standard feature and determine adjustment information for the electrolytic production parameter by the adjustment quantity confirmation model 720 based on the difference vector 710. The adjustment information of the electrolysis production parameters may include an electrolyte ratio adjustment amount 730 and/or an additive ratio adjustment amount 740.

The quality feature and the standard feature may be represented by feature vectors, each element in the feature vector of the quality feature representing a feature of the anode plate, for example, the content of a certain component of the anode plate; each element in the feature vector of the standard feature represents a feature of the standard anode plate, for example, the content of a certain component of the anode plate.

The difference vector 710 between the quality feature and the standard feature may be the difference between the feature vector corresponding to the quality feature and the feature vector corresponding to the standard feature.

The adjustment amount confirmation model 720 may be a machine learning model for determining the electrolyte ratio adjustment amount 730 and/or the additive ratio adjustment amount 740. The input of the adjustment amount confirmation model 720 may be a difference vector 710 between the quality feature and the standard feature, and the output of the adjustment amount confirmation model 720 may be an electrolyte ratio adjustment amount 730 and/or an additive ratio adjustment amount 740.

The training and model structure of the adjustment confirmation model 720 is similar to the third model, and further description of the training and model structure of the adjustment confirmation model 720 may be referred to in relation to the third model, it may be appreciated that the training sample for training the adjustment confirmation model 720 includes a difference vector between quality features and standard features of historically produced anode plates, the training sample may include feature information of historically produced unacceptable anode plates, and the tag of the training sample may include electrolyte ratio adjustment amounts and/or additive ratio adjustment amounts.

According to the method described in some embodiments of the present disclosure, the adjustment information of the electrolytic production parameters can be quickly and accurately determined according to the difference between the quality features and the standard features of the anode plate through the adjustment quantity confirmation model 720.

Referring to fig. 8, in some embodiments, the adjustment quantity confirmation model 820 may include a first adjustment quantity confirmation model 821 and/or a second adjustment quantity confirmation model 822.

The first adjustment amount confirmation model 821 may be a machine learning model for determining the electrolyte ratio adjustment amount 830. In some embodiments, the input of the first adjustment validation model 821 may be the difference vector 810 of the quality feature and the standard feature, and the output of the first adjustment validation model 821 may be the electrolyte ratio adjustment 830.

The second adjustment validation model 822 may be a machine learning model for determining the additive trim amount 840. In some embodiments, the input of the second adjustment validation model 822 may be a difference vector of the quality feature and the standard feature and the output of the second adjustment validation model 822 may be the additive trim amount 840.

The training and model structures of the first adjustment quantity confirmation model 821 and the second adjustment quantity confirmation model 822 are similar to the third model, and further description of the training and model structures of the first adjustment quantity confirmation model 821 and the second adjustment quantity confirmation model 822 may be referred to the related description of the third model, it being understood that the training sample for training the first adjustment quantity confirmation model 821 includes a difference vector between quality features and standard features of a historically produced anode plate, the training sample may include feature information of a historically produced unacceptable anode plate, and the label of the training sample may include electrolyte ratio adjustment quantity; the training samples used to train the second adjustment validation model 822 include a difference vector of quality features and standard features of historically produced anode plates, the training samples may include feature information of historically produced unacceptable anode plates, and the labels of the training samples may include additive trim amounts.

According to the method disclosed by some embodiments of the present disclosure, the electrolyte ratio adjustment amount 830 and the additive ratio adjustment amount 840 are respectively determined by two independent models, so that the structure of each model is simpler, and the task amount of model training is reduced.

Referring to fig. 9, in some embodiments, the adjustment quantity confirmation model 920 may include a first adjustment quantity confirmation model 921 and a second adjustment quantity model 922.

The first adjustment amount confirmation model 921 may be a machine learning model for determining an electrolyte ratio adjustment amount. In some embodiments, the input of the first adjustment confirmation model 921 may be the difference vector 910 of the quality feature and the standard feature, and the output of the first adjustment confirmation model 921 may be the electrolyte ratio adjustment 930.

The second adjustment validation model 922 may be a machine learning model for determining an additive trim amount. In some embodiments, the inputs of the second adjustment verification model 922 may include the difference vector 910 between the quality feature and the standard feature and the electrolyte formulation adjustment 930 output by the first adjustment verification model 921, and the output of the second adjustment verification model 922 may be the additive formulation adjustment 940.

In some embodiments, the parameters of the first tuning volume confirmation model 921 and the second tuning volume confirmation model 922 may be co-trained. For example, the samples of the joint training include a difference vector 910 of historical quality features and standard features, and the tags include historical additive trim amounts. The difference vector 910 between the history quality feature and the standard feature is input to the first adjustment amount confirmation model 921, the output of the first adjustment amount confirmation model 921 is input to the second adjustment amount confirmation model 922, the output of the second adjustment amount confirmation model 922 and the label construct a loss function, and the parameters of the first adjustment amount confirmation model 921 and the second adjustment amount confirmation model 922 are updated simultaneously based on the loss function, so that the trained first adjustment amount confirmation model 921 and second adjustment amount confirmation model 922 are obtained.

In some embodiments, the first adjustment quantity confirmation model 921 and the second adjustment quantity confirmation model 922 may also be pre-trained by the server 110 or a third party and then stored in the storage device 140, and the determination module 220 may directly call the first adjustment quantity confirmation model 921 and the second adjustment quantity confirmation model 922 from the storage device 140.

In some embodiments, the first and second adjustment

quantity validation models

921 and 922 may each include, but are not limited to, a Neural Network (NN), a Decision Tree (DT), a linear regression (Linear Regression, LR), or a combination of one or more thereof.

According to the method disclosed by some embodiments of the present disclosure, the first adjustment quantity confirmation model 921 and the second adjustment quantity confirmation model 922 are obtained through combined training, so that training of the models is simplified, training time of the models is saved, workload of model training is reduced, and total steps of training of a plurality of models are reduced; in addition, the problem that the historical electrolyte ratio adjustment amount is not easily obtained as a label can be solved, and the electrolyte ratio adjustment amount is also considered when the additive ratio adjustment amount is determined through the second adjustment amount confirmation model 922, so that repeated adjustment of the electrolyte ratio adjustment amount and the additive ratio adjustment amount is avoided, and instead, errors are increased. For example, if the sulfur component content of the anode plate produced at this time is smaller than that of the standard anode plate, if the electrolyte ratio adjustment amount and the additive ratio adjustment amount are determined by two independent machine learning models, respectively, the finally determined electrolyte ratio adjustment amount is a sulfuric acid solution increased by 1% and the additive ratio adjustment amount is thiourea increased by 1%, so that the sulfur ions are excessively added, whereas the electrolyte ratio adjustment amount determined by the first adjustment amount confirmation model 921 and the second adjustment amount confirmation model 922 is a sulfuric acid solution increased by 1% and the additive ratio adjustment amount is thiourea increased by 0.5%, so that the sulfur ions are added in an appropriate amount.

In some embodiments, the determination module 220 may determine the final adjustment information based on the adjustment information determined by the fitted curve (i.e., the first adjustment information) and the adjustment information determined by the adjustment quantity confirmation model (i.e., the second adjustment information). For example, the determination module 220 may weight sum the first adjustment information and the second adjustment information to determine final adjustment information. For example, the determination module 220 may weight sum the electrolyte ratio adjustment amount of the first adjustment information and the electrolyte ratio adjustment amount of the second adjustment information to determine the electrolyte ratio adjustment amount of the final adjustment information. For another example, the determination module 220 may weight sum the additive-mixture-ratio adjustment amount of the first adjustment information and the additive-mixture-ratio adjustment amount of the second adjustment information to determine the additive-mixture-ratio adjustment amount of the final adjustment information.

According to the method disclosed by some embodiments of the specification, the finally determined adjustment information is more accurate by fusing the adjustment information acquired based on various modes, and meanwhile, when the error of the adjustment information acquired in a certain mode is larger, the adjustment of the electrolyte proportion and the additive proportion by directly adopting the adjustment information with larger error is avoided.

In some embodiments, determining production anomaly information based on the production monitoring information may include: determining abnormal parameters of the anode plate based on the production monitoring information; and determining an abnormality type based on the abnormality determination model on the processing of the abnormality parameters of the anode plate, wherein the abnormality type comprises at least one of a die abnormality, a pouring parameter abnormality and a mine source abnormality.

The anomaly determination model 1020 can output corresponding anomaly types (e.g., die anomaly, casting parameter anomaly, mine source anomaly) by analyzing and processing the input anomaly parameters 1010 (e.g., higher impurity content, plate surface perpendicularity anomaly, etc.) of the anode plate.

In some embodiments, anomaly determination model 1020 may include various models and structures. In some embodiments, the machine learning model may include, but is not limited to, a combination of one or more of Neural Networks (NN), decision Trees (DT), linear regression (Linear Regression, LR), and the like.

In some embodiments, the obtained anomaly parameters 1010 for one or more anode plates may be used as input to an anomaly determination model 1020. The output of anomaly determination model 1020 may be an anomaly type. For example, the plate size of several anode plates may be input to be smaller than the standard size, and the anomaly type is output: the mold is abnormal.

In some embodiments, a sequence of anomaly parameters 1010 for anode plates acquired at different time periods (e.g., morning, noon, evening, etc.) may be used as input to the anomaly determination model 1020, and anomaly types 1030 corresponding to the anomaly parameters 1010 for each anode plate at different time periods may be used as output from the anomaly determination model 1020.

Parameters of anomaly determination model 1020 can be derived by training. In some embodiments, multiple sets of training samples may be obtained based on a large number of abnormal anode plate production data, each set of training samples may include multiple training data and labels to which the training data corresponds. The training data may include anomaly parameters for the anode plate and the label may be a historical anomaly type derived based on the anomaly parameters for the historical anode plate. For example, the abnormal parameters of the anode plate at a plurality of time points in a period (such as one day, one week, one month, etc.) may be collected as training data, and the abnormal type determination result (such as the abnormal type directly marked manually according to the abnormal parameters) of the abnormal parameters of the anode plate may be obtained.

In some embodiments, parameters of the anomaly determination model 1020 may be iteratively updated based on a plurality of training samples such that a loss function of the model satisfies a preset condition. For example, the loss function converges, or the loss function value is smaller than a preset value. Model training is completed when the loss function satisfies the preset conditions, resulting in a trained anomaly determination model 1020.

In some embodiments, the determination of the anomaly type based on the anomaly parameters of the anode plate may also be implemented based on other ways. For example, the determining module 220 may record the abnormal parameters of the historical anode plate and the corresponding abnormal types thereof, and query the corresponding abnormal types (such as deformation of the casting mold, and pressing ear fault) in the abnormal parameter data of the historical anode plate according to the actually obtained abnormal parameters of the anode plate (such as the verticality of the anode plate is greater than 20%), so as to obtain one or more corresponding abnormal types under the abnormal parameters corresponding to the current anode plate. For another example, after obtaining the abnormal parameters (e.g., high antimony impurity content) of the anode plate, the electrolytic production personnel (e.g., expert, technician, etc.) may rely on their own past experience to determine one or more abnormality types (e.g., abnormal component proportions, abnormal mine sources, etc.).

According to the method disclosed by the embodiments of the specification, the abnormal parameters of the anode plate are analyzed, so that the abnormal type can be rapidly checked, the production efficiency can be improved to a certain extent, and the loss caused by subsequent production is avoided.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. In addition to the application history file which is inconsistent or in conflict with the present specification, the file (this or later attached to this specification) which limits the broadest scope of the claims of this specification will also be excluded. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. An electrolytic production parameter determination method, comprising:

Acquiring electrolytic production monitoring information; the electrolytic production monitoring information at least comprises pouring detection information of the anode plate;

acquiring quality characteristics of the anode plate based on the electrolytic production monitoring information; the quality features include at least composition information of the anode plate;

determining first adjustment information by fitting a curve based on a difference between the quality feature of the anode plate and a standard feature, the standard feature including at least component information of a standard anode plate;

inputting a difference vector between the quality features and the standard features into a first adjustment quantity confirmation model, and outputting electrolyte ratio adjustment quantity;

inputting a second adjustment quantity confirmation model into the difference vector of the quality characteristics and the standard characteristics and the electrolyte ratio adjustment quantity, and outputting an additive ratio adjustment quantity;

taking the electrolyte proportion adjustment amount and the additive proportion adjustment amount as second adjustment information;

determining final adjustment information by weighted summation based on the first adjustment information and the second adjustment information, and

and determining an electrolysis production parameter based on the final adjustment information, wherein the electrolysis production parameter comprises at least one of electrolyte proportioning parameters and additive proportioning parameters.

2. The method of claim 1, the anode plate being provided with a readable information carrier, the electrolytic production monitoring information of the anode plate being obtained based on scanning the readable information carrier.

3. An electrolytic production parameter determination system comprising:

the acquisition module is used for acquiring electrolytic production monitoring information; the electrolytic production monitoring information at least comprises pouring detection information of the anode plate;

the first determining module is used for acquiring quality characteristics of the anode plate based on the electrolytic production monitoring information; the quality features include at least composition information of the anode plate;

the second determining module is used for determining first adjustment information through a fitting curve based on the difference between the quality characteristic and a standard characteristic of the anode plate, wherein the standard characteristic at least comprises component information of the standard anode plate;

determining electrolysis production parameters based on the final adjustment information; the standard features at least comprise component information of a standard anode plate; the electrolytic production parameters comprise at least one of electrolyte proportioning parameters and additive proportioning parameters.

4. A system according to claim 3, the anode plate being provided with a readable information carrier, the electrolytic production monitoring information of the anode plate being obtained based on scanning the readable information carrier.

5. An electrolytic production parameter determining device, characterized in that the device comprises at least one processor and at least one memory;

the at least one memory is configured to store computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the method of any one of claims 1-2.

6. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method as claimed in any one of claims 1 to 2.