CN117190370B

CN117190370B - Rolled copper foil processing device, method and storage medium

Info

Publication number: CN117190370B
Application number: CN202311152595.8A
Authority: CN
Inventors: 申达勤; 张利娟; 李明哲
Original assignee: Chinjet Precision Electron Co ltd
Current assignee: Chinjet Precision Electron Co ltd
Priority date: 2023-09-07
Filing date: 2023-09-07
Publication date: 2024-04-12
Anticipated expiration: 2043-09-07
Also published as: CN117190370A

Abstract

The embodiment of the specification provides a rolled copper foil processing device, a method and a storage medium, wherein the device at least comprises a dust removal module, a dehumidification module, an air filtration module, an environment sensing module and a processor: the environment sensing module is deployed at least one site of the copper foil production workshop and is used for collecting environment data, the environment sensing module comprises at least one of a dust content sensor, a humidity sensor and a temperature sensor, and the environment data comprises at least one of dust content, environment humidity data and environment temperature data; the processor is configured to: controlling an air filtering module to filter air input into a copper foil production workshop from the outside; determining dust removal parameters based on environmental data, and controlling the dust removal module to operate according to the dust removal parameters so as to remove dust in a copper foil production workshop; and determining a dehumidification parameter based on the environmental data, and controlling the dehumidification module to operate according to the dehumidification parameter so as to remove moisture in the copper foil production workshop.

Description

Rolled copper foil processing device, method and storage medium

Technical Field

The present disclosure relates to the field of copper foil production technology, and in particular, to a rolled copper foil processing device, method and storage medium.

Background

In the production process of rolled copper foil, the copper foil needs to be cleaned and subjected to antioxidation treatment in time. Lubricating oil, dust, etc. attached to the surface of the copper foil, and particles in the air may be pressed to the surface of the copper foil during the rolling process to generate pinholes. Meanwhile, wet corrosion is easy to occur on the surface of the copper material in a high-humidity environment, dry corrosion is easy to occur on the surface of the copper material in a high-temperature environment, and oxidative discoloration is easy to occur on the surface of the copper material in a short time.

In the copper foil cleaning treatment and the oxidation resistance treatment, there is a possibility of secondary pollution, thereby affecting the production quality of the copper foil. Therefore, the requirement on the temperature and humidity and the cleanliness of the environment in the production process of the rolled copper foil is high.

Therefore, it is desirable to provide a rolled copper foil processing apparatus, method and storage medium which are conducive to intelligently controlling the production environment of copper foil so as to improve the production quality of copper foil.

Disclosure of Invention

One or more embodiments of the present disclosure provide a rolled copper foil processing apparatus, which at least includes a dust removal module, a dehumidification module, an air filtration module, an environmental sensing module, and a processor, where the environmental sensing module is disposed at least one site of a copper foil production plant and is used for collecting environmental data, and the environmental sensing module includes at least one of a dust content sensor, a humidity sensor, and a temperature sensor, and the environmental data includes at least one of a dust content, an environmental humidity data, and an environmental temperature data; the processor is configured to: controlling the air filtering module to filter air which is externally input into the copper foil production workshop; determining dust removal parameters based on the environmental data, and controlling the dust removal module to operate according to the dust removal parameters so as to remove dust in the copper foil production workshop; and determining a dehumidification parameter based on the environmental data, and controlling the dehumidification module to operate according to the dehumidification parameter so as to remove moisture in the copper foil production workshop.

One or more embodiments of the present specification provide a rolled copper foil processing method, the method being performed by a processor, comprising: controlling an air filtering module to filter air input into a copper foil production workshop from the outside; determining dust removal parameters based on environmental data, and controlling a dust removal module to operate according to the dust removal parameters so as to remove dust in the copper foil production workshop; the environment data are acquired by an environment sensing module, the environment sensing module comprises at least one of a dust content sensor, a humidity sensor and a temperature sensor, and the environment data comprise at least one of dust content, environment humidity data and environment temperature data; and determining a dehumidification parameter based on the environmental data, and controlling a dehumidification module to operate according to the dehumidification parameter so as to remove moisture in the copper foil production workshop.

One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer, perform the calendered copper foil processing method of any of the embodiments described above.

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 view of a rolled copper foil treatment apparatus according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of a method of treating a rolled copper foil according to some embodiments of the present description;

FIG. 3 is an exemplary schematic diagram illustrating determining dust removal operating parameters according to some embodiments of the present disclosure;

FIG. 4 is an exemplary schematic diagram illustrating determining joint operating parameters according to some embodiments of the present description.

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 view showing the structure of a rolled copper foil treatment apparatus according to some embodiments of the present specification.

As shown in fig. 1, in some embodiments, the rolled copper foil processing apparatus 100 includes at least a dust removal module 110, a dehumidification module 120, an air filtration module 130, an environmental sensing module 140, and a processor 150.

The dust removal module 110 refers to a device or component for removing dust. The dust removal module 110 may be used to remove dust from the copper foil production plant.

In some embodiments, the dust module 110 may include at least one negative pressure dust collector (e.g., negative pressure dust collector 1, negative pressure dust collectors 2, … …, negative pressure dust collector n).

The negative pressure dust removing device can filter and clean particulate matters in the air by a negative pressure principle.

In some embodiments, the negative pressure dust removal device includes at least a negative pressure blower unit (not shown in fig. 1) and a filter unit (not shown in fig. 1).

The negative pressure fan unit may be used to suck air in the copper foil production plant.

The filter unit may be used to filter the inhaled air. In some embodiments, the filter unit may be used for dust, impurity filtration of the inhaled air.

The negative pressure dust collector can suck the air containing particles into the negative pressure dust collector through the negative pressure generated by the negative pressure fan unit, and the particles are separated and collected through the filtering effect of the filtering unit, so that the purified air is finally discharged.

In some embodiments, at least one negative pressure dust removal device may be deployed at least one site in a copper foil production plant.

In some embodiments, the number of deployments and deployment locations of the negative pressure dust collector may be predetermined. For example, the number of the negative pressure dust collectors can be determined according to the size of the space of the copper foil production plant, and the larger the space of the copper foil production plant is, the larger the number of the negative pressure dust collectors is. For another example, a plurality of sites may be preset in a copper foil production plant, and the sites are determined as deployment positions of the negative pressure dust removing device. The site may be preset by the system or by human.

In some embodiments, the number and location of deployments of the negative pressure dust collector is related to the spatial characteristics of the copper foil production plant.

In some embodiments, the spatial features may include a spatial size (e.g., dimensions, volume, etc.) of the copper foil production plant, an internal equipment location, etc. The internal equipment location refers to the location of the relevant equipment deployed at the copper foil production plant. Related equipment includes dust removal modules, dehumidification modules, air filtration modules, environmental sensing modules, and the like, as well as inherent equipment within a copper foil production plant, such as machines for calendaring copper foil, and the like.

In some embodiments, the processor may establish a coordinate system for the copper foil production plant to determine the internal device location. For example only, the coordinate system may be a three-dimensional coordinate system having an X-axis, a Y-axis, and a Z-axis. The ground surface is an X-Y plane, and the Z axis can be perpendicular to the X-Y plane.

In some embodiments, the processor may also obtain spatial features of the copper foil production plant through a modeling algorithm.

In some embodiments, the processor may construct a deployment feature vector based on the spatial features of the copper foil production plant, external environmental data, and determine a dust removal deployment scheme of the at least one negative pressure dust removal device via the deployment vector database. The dust removal deployment scheme comprises the deployment number and deployment positions of the negative pressure dust removal devices.

The external environment data refers to data and/or information related to the natural environment outside the copper foil production plant. In some embodiments, the external environment data may include a geographic location of the copper foil production plant (e.g., in north, in south, etc.), an average dust content of the external environment, and so forth.

The elements of the deployment feature vector may include spatial features of the copper foil production plant, external environmental data. For example, the deployment feature vector may be (a, B), where a represents a spatial feature of the copper foil production plant and B represents external environmental data of the copper foil production plant.

In some embodiments, the processor may determine the associated deployment feature vector based on vector matching of the deployment feature vector in the deployment vector database. In some embodiments, a plurality of reference deployment feature vectors and their corresponding dust removal deployment schemes (including number of deployments, deployment locations) may be included in the deployment vector database. In some embodiments, the reference deployment feature vector in the deployment vector database may be constructed based on historical data. For example, multiple reference deployment feature vectors may be derived by vector construction of multiple historical data (including historical spatial features and historical external environmental data). The dust removal deployment scheme corresponding to the reference deployment feature vector can be obtained according to the historical deployment condition in the historical data.

In some embodiments, the processor may determine a reference deployment feature vector that meets a preset matching condition in the deployment vector database based on the deployment feature vector, and determine the reference deployment feature vector that meets the preset matching condition as an associated deployment feature vector; and determining the deployment number and the deployment positions of the negative pressure dust removal devices according to the dust removal deployment schemes corresponding to the associated deployment feature vectors. In some embodiments, the preset matching condition may include a vector distance less than a distance threshold, a vector distance minimum, and the like.

The dehumidifying module 120 refers to a device or part for removing moisture from air. The dehumidifying module 120 may be used to remove moisture from the air in the copper foil production plant and reduce the air humidity.

In some embodiments, the dehumidification module 120 may include at least one dehumidifier (e.g., dehumidifier 1, dehumidifier 2, … …, dehumidifier n). The dehumidifier may be used to suck air and remove moisture from the air, and to discharge dry air.

In some embodiments, at least one dehumidifier may be deployed at least one site in the copper foil production plant.

In some embodiments, the number of deployments and deployment locations of the dehumidifier may be predetermined. For example, the number of the disposed dehumidifiers may be determined according to the size of the space of the copper foil production plant, and the larger the space of the copper foil production plant, the larger the number of the disposed dehumidifiers. For another example, a copper foil production plant may be pre-set with a plurality of sites that are determined as the deployment locations of the dehumidifiers. The site may be preset by the system or by human.

In some embodiments, the number of deployments and deployment locations of the dehumidifier are related to the space parameters of the copper foil production plant. The space parameters of the copper foil production plant refer to parameters related to the space of the copper foil production plant. For example, the spatial parameters may be the size and volume of a copper foil production plant. In some embodiments, the spatial parameters may be determined based on a construction drawing of a copper foil production plant.

In some embodiments, the processor may model the copper foil production plant based on the spatial parameters; based on the spatial modeling of the copper foil production plant, the deployment number and deployment position of at least one dehumidifier in the copper foil production plant are determined. The space modeling comprises the length, width and height of the space.

In some embodiments, the processor may determine a reference spatial modeling similar to the current spatial modeling in the standard assembly database based on the spatial modeling of the copper foil production plant, determine a deployment number and deployment location of the at least one dehumidifier according to a dehumidifier deployment scheme corresponding to the reference spatial modeling. The standard assembly database may include correspondence between a plurality of reference spatial modeling and a plurality of dehumidifier deployment schemes, and may be preset based on historical data or a priori knowledge. For example, reference space modeling 1, reference space modeling 2, … …, and reference space modeling n are stored in the standard assembly database, distances between the length, width, and height of each reference space modeling and the length, width, and height of the copper foil production plant are compared, and a deployment scheme corresponding to the reference space modeling with the smallest distance between the copper foil production plants is determined as a deployment scheme of the dehumidifier.

The air filtering module 130 refers to a device or component for filtering air.

In some embodiments, the air filtration module may coarsely filter air externally input into the copper foil production plant. For example, the coarse filtration may be to primarily remove dust, impurities, etc. from the outside air, so that the dust removal module may further process the air filtered in the copper foil production plant.

The environmental sensing module 140 refers to a device or component for collecting environmental data. The environmental sensing module 140 may be used to collect environmental data inside the copper foil production plant.

In some embodiments, the environmental sensing module 140 may be deployed at least one site in a copper foil production plant. In some embodiments, the number of deployments and deployment locations of environmental sensing modules 140 may be related to the spatial characteristics of the copper foil production plant. The number of deployments and the determination manners of the deployment positions of the environmental sensing modules 140 are similar to those of the negative pressure dust removing devices, and are not described herein.

In some embodiments, the environmental sensing module 140 may include at least one of a dust content sensor 141, a humidity sensor 142, and a temperature sensor 143. Accordingly, the collected environmental data may include at least one of dust content, environmental humidity data, and environmental temperature data.

In some embodiments, the environmental sensing module 140 may acquire environmental data under the control of a processor.

In some embodiments, the dust content sensor 141 may include at least one, and the dust content sensor 141 may be deployed at least one site in the copper foil production plant. In some embodiments, at least one dust content sensor 141 may collect dust content at a corresponding deployment location at least one point in time, the collected dust content plurality comprising a dust content sequence. For example, the dust content sequence may be [ (m 1, t1, f 11), (m 1, t2, f 12), (m 2, t1, f 21), (m 2, t2, f 22) ], where m1 refers to dust monitoring site 1, m2 refers to dust monitoring site 2, t1 refers to time point 1, t2 refers to time point 2, f11 refers to the dust content of dust monitoring site 1 at time point l, f12 refers to the dust content of dust monitoring site 1 at time point 2, and f21, f22 are the same. Wherein the dust monitoring site is a deployment location of the dust content sensor.

In some embodiments, the humidity sensor 142 may include at least one, and the humidity sensor 142 may be deployed at least one site in a copper foil production plant. In some embodiments, at least one humidity sensor 142 may collect ambient humidity data at the corresponding deployment location at least one point in time, the plurality of ambient humidity data collected comprising an ambient humidity sequence. For example, the ambient humidity sequence may be [ (n 1, t1, s 11), (n 1, t2, s 12), (n 2, t1, s 21), (n 2, t2, s 22) ], where n1 refers to humidity monitoring site 1, n2 refers to humidity monitoring site 2, t1 refers to time point 1, t2 refers to time point 2, s11 refers to ambient humidity data of humidity monitoring site 1 at time point 1, s12 refers to ambient humidity data of humidity monitoring site 1 at time point 2, s21, s22 are the same. Wherein the humidity monitoring site is a deployment location for the humidity sensor.

In some embodiments, the temperature sensor 143 may include at least one, and the temperature sensor 143 may be disposed at least one site of the copper foil production plant. In some embodiments, at least one temperature sensor 143 may collect ambient temperature data at the corresponding deployment location at least one point in time, the plurality of ambient temperature data collected comprising an ambient temperature sequence. For example, the ambient temperature sequence may be [ (k 1, t1, j 11), (k 1, t2, j 12), (k 2, t1, j 21), (k 2, t2, j 22) ], where k1 refers to temperature monitoring site 1, k2 refers to temperature monitoring site 2, t1 refers to time point 1, t2 refers to time point 2, j11 refers to ambient temperature data of temperature monitoring site 1 at time point 1, j12 refers to ambient temperature data of temperature monitoring site 1 at time point 2, j21, j22 are the same. Wherein the temperature monitoring site is a deployment location of the temperature sensor.

In some embodiments, the environmental sensing module 140 may also include an air flow monitoring device (not shown in fig. 1).

An air flow monitoring device refers to a device for monitoring air change data. The air flow monitoring device can enable air flow in the copper foil production workshop to be visualized so as to monitor air flow conditions in the copper foil production workshop.

The air change data refers to data related to the flow of air in the space. For example, the air change data may include air flow rate, air flow direction, temperature change, and the like.

In some embodiments, the air flow monitoring device may acquire air change data under control of the processor.

In some embodiments, the environmental sensing module 140 may also include an air composition detector (not shown in fig. 1). An air composition monitor refers to a device or component for acquiring air composition data. The air composition monitor may be used to obtain air composition data in a copper foil production plant.

The air composition data refers to data and/or information reflecting the content of each composition in the air in the copper foil production plant. In some embodiments, the air composition data may include gas composition type, volume fraction and mass fraction of each gas composition, and the like. For example, the air composition data may be [ (nitrogen, volume fraction A1%, mass fraction B1%) ], (oxygen, volume fraction A2%, mass fraction B2%) ].

In some embodiments, the air composition detector may acquire air composition data under control of the processor.

Processor 150 refers to a device or means for processing data and/or information obtained from other modules or apparatus components. The processor 150 may process information and/or data relating to the rolled copper foil processing apparatus 100, and execute program instructions based on such data, information, and/or processing results to perform one or more of the functions described herein.

In some embodiments, the processor 150 may control the air filtering module 130 to filter air externally input into the copper foil production plant. In some embodiments, the processor 150 may determine dust removal parameters based on environmental data and control the dust removal module 110 to operate in accordance with the dust removal parameters to remove dust from the copper foil production plant. In some embodiments, the processor 150 may determine dehumidification parameters based on the environmental data, and control the dehumidification module 120 to operate in accordance with the dehumidification parameters to purge moisture from the copper foil production plant.

In some embodiments, the processor 150 may control other modules or device components in the calendered copper foil processing device 100 to adjust the internal environment of the copper foil production space before, during, and/or during the cleaning process after the copper foil production process.

As shown in fig. 1, in some embodiments, the calendered copper foil processing apparatus 100 may further include an optical camera 160.

The optical camera 160 may be used to photograph the copper foil to acquire image data.

In some embodiments, the processor 150 may control the optical camera 160 to capture images of the copper foil to obtain image data. In some embodiments, the processor 150 may control the optical camera 160 to photograph the copper foil according to certain photographing parameters to acquire image data. For example, the photographing parameters may include photographing time, photographing time interval, photographing position, and the like.

As shown in fig. 1, in some embodiments, the calendered copper foil processing apparatus 100 may further include a plasma purge module 170.

The plasma purge module 170 refers to a device or component for cleaning dirt. In some embodiments, the plasma purge module 170 may be used to clean the surface of the copper foil from adhering matter (e.g., grease, dust, etc.) during the cleaning process after the copper foil is produced.

In some embodiments, the processor 150 may determine the purge parameters and control the plasma purge module 170 to operate according to the purge parameters to remove surface deposits from the copper foil.

The above parts can establish data connection by wired or wireless means. For more control of the processor see below.

It should be noted that the above description of the rolled copper foil processing apparatus and the modules thereof is for convenience of description only, and the present description is not limited 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.

Figure 2 is an exemplary flow chart of a method of treating a rolled copper foil according to some embodiments of the present description. In some embodiments, the process 200 may be performed by a processor. As shown in fig. 2, the process 200 includes the following steps.

At step 210, the air filtration module is controlled to filter air inputted into the copper foil production plant from the outside.

In some embodiments, the processor may control the air filtration module to operate according to the filtration parameters to filter air externally input into the copper foil production plant.

The filtering parameters refer to working parameters adopted when the air filtering module operates. The filter parameters may include filter run time, filter run power. In some embodiments, the filter parameters may be preset based on a priori knowledge. In some embodiments, the filter parameters may be determined based on historical data. For example, the historical filter parameter that is most used may be determined as the current filter parameter.

And 220, determining dust removal parameters based on the environmental data, and controlling the dust removal module to operate according to the dust removal parameters so as to remove dust in the copper foil production workshop.

The environmental data refers to data related to the internal environment of the copper foil production plant.

In some embodiments, the processor may control the environmental sensing module to collect environmental data. In some embodiments, when the environmental sensing module includes at least one of a dust content sensor, a humidity sensor, a temperature sensor, the environmental data includes at least one of a dust content, an environmental humidity data, an environmental temperature data.

In some embodiments, when the environmental sensing module includes an air flow detection device, the environmental data may also include air change data. In some embodiments, when the environmental sensing module includes an air composition detector, the environmental data may also include air composition data.

For more description of environmental data, see fig. 1 and the description thereof.

Dust refers to fine particles present in the copper foil production plant. Dust can affect the production accuracy of the copper foil, such as airborne dust can be pressed against the foil surface during the calendaring process to create pinholes.

The dust removal parameter refers to a working parameter adopted when the dust removal module operates.

In some embodiments, the dust removal parameters may include at least one of dust removal operation time, dust removal operation power, and dust removal operation mode of the dust removal module.

The dust removal operation mode refers to an operation mode adopted when the dust removal module operates. In some embodiments, the dust removal modes of operation may include a continuous dust removal mode and a intermittent dust removal mode. The continuous dust removal mode refers to continuous dust removal operation during operation of the dust removal module. The intermittent dust removal mode refers to that the dust removal module performs dust removal operation at intervals during operation. The interval for performing the dust removing operation may be preset empirically by those skilled in the art.

The dust removal parameters may be determined in a number of ways. In some embodiments, the dusting parameters may be preset based on a priori knowledge. In some embodiments, the dust removal parameters may be determined based on historical data. For example, the historical dust removal parameter that is most used may be determined as the current dust removal parameter.

In some embodiments, the processor may determine the dust removal parameters based on the environmental data through a machine learning model or various data analysis algorithms. See fig. 3 for a more description of this embodiment.

In some embodiments, when the dedusting module and the dehumidification module work in combination, the processor may determine the joint operating parameters (including the joint dedusting parameters and the joint dehumidification parameters) via a machine learning model or various data analysis algorithms. See fig. 4 for more description of determining joint operating parameters.

Step 230, determining a dehumidification parameter based on the environmental data, and controlling the dehumidification module to operate according to the dehumidification parameter to remove moisture in the copper foil production plant.

The dehumidification parameters refer to working parameters adopted when the dehumidification module operates.

In some embodiments, the dehumidification parameters may include at least one of dehumidification operating power, dehumidification operating time of the dehumidification module.

The dehumidification parameters may be determined in a number of ways. In some embodiments, the dehumidification parameters may be preset based on a priori knowledge. In some embodiments, the dehumidification parameters may be determined based on historical data. For example, the historical dehumidification parameter that is most used may be determined as the current dehumidification parameter.

In some embodiments, the processor may determine the dehumidification parameters from a vector database based on the environmental data.

In some embodiments, the processor may construct the dehumidification feature vector based on the air composition data, the ambient temperature data, and the ambient humidity data; based on the dehumidification feature vector, a dehumidification parameter is determined by a dehumidification vector database. See fig. 1 for a relevant description of air composition data.

The elements of the dehumidifying feature vector may include air composition data, ambient temperature data, and ambient humidity data of the copper foil production plant. For example, the dehumidification feature vector may be (x 1, y1, z 1), where x1 is air composition data, y1 is ambient temperature data, and z1 is ambient humidity data.

A dehumidification vector database refers to a database for storing, indexing, and querying vectors. In some embodiments, the dehumidification vector database stores at least one reference dehumidification feature vector and corresponding reference dehumidification parameters. In some embodiments, the reference dehumidification feature vector in the dehumidification vector database may be constructed based on historical data, for example, the reference dehumidification feature vector may be constructed based on historical air composition data, historical ambient temperature data, and historical ambient humidity data, and the reference dehumidification parameter corresponding to the reference dehumidification feature vector may be determined from the corresponding historical dehumidification parameter.

In some embodiments, the processor may determine a reference dehumidification feature vector that meets a preset matching condition in the dehumidification vector database based on the dehumidification feature vector, and determine the reference dehumidification feature vector that meets the preset matching condition as an associated dehumidification feature vector; and determining the current dehumidification parameters of the dehumidifier according to the reference dehumidification parameters corresponding to the associated dehumidification characteristic vectors. In some embodiments, the preset matching condition may include a vector distance less than a distance threshold, a vector distance minimum, and the like.

In one embodiment of the specification, based on the environmental temperature data, the environmental humidity data and the air composition data in the copper foil production workshop, the air humidity condition in the copper foil production workshop can be accurately reflected, so that the dehumidification parameters of the dehumidifier can be determined in a targeted manner, the moisture in the air can be effectively removed, the air in the copper foil production workshop is kept dry, the oxidation speed of the surface of the copper foil is slowed down, and the production quality of the copper foil is guaranteed.

In some embodiments, when the dedusting module and the dehumidification module work in combination, the processor may determine the joint operating parameters (including the joint dedusting parameters and the joint dehumidification parameters) via a machine learning model or various data analysis algorithms. For a detailed description of determining the joint operating parameters, see FIG. 4 and its description.

In the embodiment of the specification, the processor is used for intelligently controlling the related modules before and after the copper foil production process and in the production process, the environment of the copper foil production workshop is adjusted, the environment of the copper foil production workshop can be effectively adjusted, adverse effects of the workshop environment on the copper foil production quality are reduced, and the defective rate is reduced.

In some embodiments, the processor may determine the purge parameters and control the plasma purge module to operate according to the purge parameters to remove surface deposits from the copper foil.

The surface deposit means impurities adhering to the surface of the copper foil. For example, grease, dust, or the like adhering to the surface of the copper foil.

In some embodiments, the processor may control the optical camera to acquire image data, and based on the image data, monitor the surface deposit of the copper foil using an image recognition algorithm. Exemplary image recognition algorithms include edge detection algorithms, color/texture feature analysis algorithms, support vector machine based image detection algorithms, and the like.

See fig. 1 for a relevant description of an optical camera, image data.

The purging parameter refers to the working parameter adopted when the plasma purging module operates. In some embodiments, the purge parameters may include whether to turn on the plasma purge device and at least one of a purge run time, a purge run power after turn on.

In some embodiments, the purge parameters may be preset based on a priori knowledge. For example, the plasma purge device may be turned on after the copper foil production is completed.

In some embodiments, the purge parameter may be determined based on historical data. For example, after determining to turn on the plasma purge device, the historical purge operation time and the historical purge operation power with the largest number of uses may be determined as the current purge parameters.

In some embodiments, the processor may predict a dust content index at a future point in time after the dust removal module operates according to the dust removal parameters, and a humidity index at a future point in time after the dehumidification module operates according to the dehumidification parameters; starting a plasma purging module in response to the dust content index and/or the humidity index not meeting preset judging conditions; in response to turning on the plasma purge module, a purge run time and/or a purge run power is determined based on the dust content index and the humidity index.

The dust content index refers to a quantitative value for evaluating dust conditions. The wetness index refers to a quantified value used to evaluate wetness.

In some embodiments, the processor may determine the estimated dust content and the estimated humidity data at a future point in time based on the environmental data, the dust removal parameter, and the dehumidification parameter at the current point in time; and determining a dust content index and a humidity index based on the estimated dust content and the estimated humidity data respectively.

The estimated dust content refers to the dust content of a copper foil production workshop at a future time point after the predicted dust removal module operates according to dust removal parameters. The estimated humidity data refers to humidity data of a copper foil production workshop at a future time point after the predicted dehumidification module operates according to dehumidification parameters.

In some embodiments, the processor may determine the estimated dust content and the estimated humidity data at a future point in time based on the environmental data, the dust removal parameters, and the dehumidification parameters via a machine learning model.

In some embodiments, the processor may input air change data, dust content, ambient humidity data, ambient temperature data, dedusting parameters, and dehumidification parameters at a current point in time into a joint determination model to obtain future joint data at a future point in time; and determining estimated dust content and/or estimated humidity data based on future joint data output by the joint determination model. In this embodiment, the future joint data may be data for which the future joint data sequence corresponds at a future point in time. In this embodiment, when the dust removing device is not turned on, the dust removing parameter may be set to 0 and then input into the joint determination model.

For more explanation of the joint determination model, future joint data sequences see FIG. 4 and its description.

In some embodiments, the processor may determine a class of reject sites at a future point in time and a dust content mean based on the estimated dust content and the dust content threshold; the dust content index is determined based on the number of a class of reject sites and the dust content mean. For example, the processor may determine a weighted result of the number of a class of reject sites and the average of dust content as a dust content index,

One type of reject site refers to a dust monitoring site where the dust content exceeds a dust content threshold. The processor can determine dust monitoring points with dust content exceeding a dust content threshold value as a type of disqualified points in a plurality of dust monitoring points included in the estimated dust content. The dust content average value refers to the average value of dust content at each dust monitoring site in future time points. The weighting weights may be preset by human or system. The dust content threshold value is a threshold value for judging whether the dust content satisfies the requirement. In some embodiments, the dust content threshold may be determined based on production requirements. The dust content threshold may also be determined in other possible ways, without limitation.

In some embodiments, the processor may determine the class two reject sites at the future point in time and the ambient humidity mean based on the pre-estimated humidity data and the humidity threshold; the wetness index is determined based on the number of sites of the second type of failure and the ambient wetness mean. For example, the processor may determine a weighted result of the number of the class II reject sites and the ambient humidity mean as the humidity index.

Wherein, the second type of unqualified site refers to a humidity monitoring site with the environmental humidity exceeding a humidity threshold. The ambient humidity mean refers to the mean of the ambient humidity at each humidity monitoring site at a future point in time. The weighting weights may be preset by human or system. The humidity threshold is a threshold for determining whether humidity satisfies a requirement. In some embodiments, the humidity threshold may be determined based on production requirements. The humidity threshold may also be determined in other possible ways, without limitation.

In some embodiments, the processor may determine whether the dust content index and/or the humidity index satisfy a preset determination condition, and determine whether to turn on the plasma purge module according to the determination result.

In some embodiments, the preset determination condition may include the dust content index being less than a first index threshold. In some embodiments, the preset determination condition may include the wetness index being less than a second index threshold. In some embodiments, the preset determination condition may be at least one of the two cases. Wherein the first index threshold is a threshold related to the dust content index, and the second index threshold is a threshold related to the humidity index. The first index threshold and the second index threshold may be preset by a person or a system.

In some embodiments, the processor may turn on the plasma purge module when the determination is that the dust content index is less than the first index threshold and/or the humidity index is less than the second index threshold.

In some embodiments, after determining to turn on the plasma purge module, the processor may determine purge parameters (including purge run time and/or purge run power) from a preset look-up table based on the dust content index and the humidity index. The preset comparison table comprises the corresponding relations of various dust content indexes, various humidity indexes, various purging operation times and various purging operation powers. For example, the correspondence may be that the larger the dust content index, the larger the humidity index, the longer the purge operation time, and the larger the purge operation power.

For more description of dust content index and moisture index see fig. 3, fig. 4 and their related description.

In the embodiment of the specification, through estimating dust content index and humidity index at future time points, whether the plasma purging device is started or not can be judged in advance, the surface of the copper foil is purged in time, an optimal purging scheme is selected for purging, the influence of attachments on the surface of the copper foil on production precision is prevented, intelligent control is carried out on copper foil production, copper foil production quality is improved, and production resource consumption is reduced.

Meanwhile, when grease and dust attached to the surface of the copper foil are removed, the production environment of the copper foil can be intelligently regulated and controlled by controlling the operation of related modules, the secondary pollution of the copper foil after cleaning and/or antioxidation treatment is avoided, and the production quality of the copper foil is further improved.

It should be noted that the above description of the process 200 is for illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 200 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. 3 is an exemplary schematic diagram illustrating determining dust removal operating parameters according to some embodiments of the present description.

Referring to fig. 3, in some embodiments, the processor may determine at least one candidate dust removal parameter 313; determining a first evaluation parameter 360 based on the air change data 311, the space parameter 312 of the copper foil production plant, the candidate dust removal parameter 313, the dust content 314, and the dust content threshold 340; based on the first evaluation score 360, a dust removal parameter 370 is determined from the at least one candidate dust removal parameter 313.

The candidate dust removal parameter refers to a parameter to be determined as a final dust removal parameter.

The candidate dust removal parameters may be determined in a number of ways. In some embodiments, candidate dust removal parameters may be preset for a worker. In some embodiments, candidate dust removal parameters may be generated based on historical dust removal records. For example, the most frequently used dust removal parameter m is used in the history, and the processor may appropriately adjust the dust removal operation time, the dust removal operation power and the dust removal operation mode of the dust removal parameter m to generate at least one candidate dust removal parameter.

Referring to fig. 3, in some embodiments, the processor may determine a future dust content sequence 330 by a dust removal parameter determination model 320 based on the air change data 311, the space parameters 312 of the copper foil production plant, the dust content 314, the candidate dust removal parameters 313; a first evaluation score 360 is determined based on the future dust content sequence 330, the dust content threshold 340.

In some embodiments, the dusting parameter determination model may be a machine learning model of the custom structure hereinafter. The dust removal parameter determination model may also be a machine learning model of other structures, such as a neural network model or the like.

Referring to fig. 3, in some embodiments, the dust removal parameter determination model 320 may include an air change prediction layer 321 and a dust content prediction layer 323. In some embodiments, the air change prediction layer and the dust content prediction layer may be deep neural networks (Deep Neural Networks, DNN).

Referring to fig. 3, in some embodiments, the inputs to the air change prediction layer 321 may include air change data 311, spatial parameters 312, candidate dust removal parameters 313, and the output may be future air change data 322.

In some embodiments, the air change data input to the air change prediction layer may include current monitored air change data and historical period monitored air change data.

For more description of air change data, spatial parameters see fig. 1 and its associated description.

Future air change data refers to predicted air change data for a future time period.

Referring to fig. 3, in some embodiments, the inputs to the dust content prediction layer 323 may include future air change data 322, dust content 314, and candidate dust removal parameters 313, and the output may be a future dust content sequence 330.

In some embodiments, the dust content of the input dust content prediction layer may be data of the current time monitoring. For more explanation of dust content, see fig. 2 and its associated description.

In some embodiments, the future dust content sequence includes dust content collected by the dust content sensor at different dust monitoring sites at least one future point in time within the future time period. See fig. 1 for a more description of dust content sequences, dust monitoring sites.

In some embodiments, the air change prediction layer may be trained based on a first training sample with a first label. In some embodiments, the first training sample may include sample air change data of a first sample period, space data of the sample, and a sample dust removal parameter, and the first label may be air change data of a second sample period corresponding to the first training sample. The first sample period is located before the second sample period. In some embodiments, the first training sample and the first tag may be derived based on historical data. For example, the air change data, the spatial data, and the dust removal parameter of the first history period may be used as a first training sample, and the air change data of the second history period may be used as a first label, where the first history period is located before the second history period.

In some embodiments, the dust content prediction layer may be trained based on a second training sample with a second label. In some embodiments, the second training sample may include sample air variation data for a fourth sample period, sample dust content for a third sample period, sample dust removal parameters, and the second label may be a sequence of actual dust content for the fourth sample period corresponding to the second training sample. The third sample period is located before the fourth sample period. In some embodiments, the second training sample, the second label, may be derived based on historical data. For example, the air change data of the fourth history period, the dust content of the third history period, and the dust removal parameter may be used as a second training sample, and the actual dust content sequence of the fourth history period may be used as a second label, where the third history period is located before the fourth history period.

The training process of the model may not be performed in the processor of the rolled copper foil processing apparatus. The training process of the model can be carried out in an external server, and after the training by the external server, the model is preset into a processor of the rolled copper foil processing device.

Referring to fig. 3, in some embodiments, the processor may determine a dust content index 350 based on the future dust content sequence 330, the dust content threshold 340. See fig. 2 for a more description of dust content thresholds.

In some embodiments, the processor may select a target time point in a future time period, determine a class of reject sites and a dust content mean for the target time point, and determine the dust content index based on the number of class of reject sites and the dust content mean.

In some embodiments, the processor may determine a future point in time at which dust removal ends from a plurality of future points in time corresponding to the sequence of future dust contents as the target point in time.

The type of disqualified sites at the target time point refers to dust monitoring sites with dust content exceeding a dust content threshold value in each dust monitoring site at the target time point.

The average dust content of the target time point is the average dust content of each dust monitoring site at the target time point.

See fig. 2 for a more description of a class of reject sites, dust content mean, dust content index.

Referring to fig. 3, in some embodiments, the processor may determine a first evaluation score 360 based on the dust content index 350 and the candidate dust removal parameter 313.

The first evaluation score refers to a quantized value for evaluating the merits of candidate dust removal parameters.

In some embodiments, the processor may calculate a dust removal cost based on the candidate dust removal parameters, such as calculating power consumption based on the dust removal operation time and the dust removal operation power, with the power consumption being the dust removal cost. Further, the processor may determine a weighted result of the dust content index and the dust removal cost as the first evaluation score, wherein the weighted weight may be preset by a person or a system.

Referring to fig. 3, in some embodiments, the processor may determine the dust removal parameter 370 based on the first evaluation score 360. In some embodiments, the processor may select the candidate dusting parameter with the smallest first evaluation score as the final dusting parameter.

According to some embodiments of the specification, the dust removal parameter determination model is used for processing air change data, space data and candidate dust removal parameters, so that a rule can be found from a large amount of related data by utilizing the self-learning capability of the machine learning model, the association relationship between a future dust content sequence and the related data is obtained, and the accuracy and the efficiency of determining the future dust content sequence are improved; and the dust removal parameters are determined from the candidate dust removal parameters based on the first evaluation score, so that the dust removal parameters can be dynamically selected according to the dust content and the power consumption cost, and the dust removal efficiency and the energy saving effect are improved.

In some embodiments, the dedusting module and the dehumidification module may determine joint operating parameters (including joint dedusting parameters and joint dehumidification parameters) when joint operation is desired in the future. The combined dedusting parameters are dedusting parameters of the dedusting module when the dedusting module and the dehumidifying module work in a combined mode, and the combined dehumidifying parameters are dehumidifying parameters of the dehumidifying module when the dedusting module and the dehumidifying module work in a combined mode.

Referring to fig. 4, in some embodiments, the processor may determine at least one candidate dust removal parameter 313 and at least one candidate dehumidification parameter 413; determining a future joint data sequence 430 by a joint determination model 420 based on the future air change data 322, the dust content 314, the ambient humidity data 411, the ambient temperature data 412, the candidate dehumidification parameters 413, the candidate dust removal parameters 313; determining a second evaluation score 470 based on the future joint data sequence 430, the dust content threshold 340, the humidity threshold 440, the candidate dust removal parameter 313, and the candidate dehumidification parameter 413; based on the second evaluation score 470, a combined dust removal parameter 480 and a combined dehumidification parameter 490 are determined.

For more description of candidate dust removal parameters, future air change data, dust content threshold, see fig. 3 and its associated description.

The joint determination model may be a machine learning model. For example, a deep neural network (Deep Neural Networks, DNN) model, a recurrent neural network (Recurrent Neural Network, RNN) model, or the like, or any combination thereof.

Referring to fig. 4, in some embodiments, the inputs to the joint determination model 420 are future air change data 322, dust content 314, ambient humidity data 411, ambient temperature data 412, candidate dehumidification parameters 413, candidate dust removal parameters 313, and output as a future joint data sequence 430.

In some embodiments, the dust content, the ambient humidity data, the ambient temperature data of the input joint determination model may be data obtained by monitoring the current time. For more description of dust content, ambient humidity data, ambient temperature data see fig. 2 and its associated description.

The candidate dehumidification parameters refer to parameters to be confirmed as final joint dehumidification parameters. In some embodiments, the processor may determine candidate dehumidification parameters by way of vector matching based on a dehumidification vector database. For more explanation of dehumidification vector database and vector matching, see fig. 2 and its associated description.

Future joint data sequences refer to environmental change data after joint work is performed in the future.

In some embodiments, the future joint data sequence may include a future dust content sequence and a future ambient humidity sequence. In this embodiment, the future dust content sequence output by the joint determination model is different from the future dust content sequence output by the dust content prediction layer. The influence of dehumidification parameters, environmental temperature data and environmental humidity data is further considered by the combined determination model relative dust content prediction layer.

In some embodiments, the future sequence of environmental humidity includes at least one future point in time over a future period of time, environmental humidity data collected at different humidity monitoring sites. See fig. 1 for more description of the environmental humidity sequence, humidity monitoring sites.

In some embodiments, the joint determination model may be trained based on a third training sample with a third tag. In some embodiments, the third training sample may include sample air variation data of the sixth sample period and sample dust content, sample ambient humidity data, sample ambient temperature data, sample dehumidification parameters, sample dust removal parameters of the fifth sample period, and the third label may be an actual dust content sequence and an actual ambient humidity sequence of the sixth sample period corresponding to the third training sample. The fifth sample period is located before the sixth sample period.

In some embodiments, a third training sample, a third tag, may be derived based on historical data. For example, the air change data of the sixth history period, the dust content of the fifth history period, the ambient humidity data, the ambient temperature data, the dehumidification parameter, the dust removal parameter may be used as a third training sample, the actual dust content sequence and the actual ambient humidity sequence of the sixth history period may be used as a third label, and the fifth history period may be located before the sixth history period.

Referring to fig. 4, in some embodiments, the processor may determine a wetness index 450 based on the future joint data sequence 430 and the wetness threshold 440; determining a dust content index 460 based on the future joint data sequence 430 and the dust content threshold 340; a second evaluation score 470 is determined based on the humidity index 450, the dust content index 460, the candidate dust removal parameter 313, and the candidate dehumidification parameter 413. See fig. 2 for more description of humidity threshold.

It should be noted that the dust content index 460 of fig. 4 is different from the dust content index 350 of fig. 3, the dust content index 460 is determined based on the future joint data series 430 and the dust content threshold 340, and the dust content index 350 is determined based on the future dust content series 330 and the dust content threshold 340.

In some embodiments, the processor may select a target time point in a future time period, determine a class II unacceptable site and an ambient humidity mean for the target time point, and determine the humidity index based on the number of class II unacceptable sites and the ambient humidity mean.

See fig. 3 for more description of the target time point. See fig. 2 for more description of the sites of class II failure, the ambient humidity mean, the humidity index.

The second evaluation score refers to a quantized value for evaluating the merits of the candidate dust removal parameter and the candidate dehumidification parameter under the combined working condition.

In some embodiments, the processor may determine the power consumption as the dehumidification cost based on the dehumidification operation time and the dehumidification operation power of the candidate dehumidification parameters; and determining a weighted result of the dust content index, the dust removal cost, the humidity index and the dehumidification cost as a second evaluation score. See fig. 3 and its associated description for more description of dust removal costs.

Referring to fig. 4, in some embodiments, the processor may determine the joint de-dusting parameter 480 and the joint de-dehumidifying parameter 490 based on the second evaluation score 470. In some embodiments, the processor may select the candidate dust removal parameter and the candidate dehumidification parameter with the smallest second evaluation score as the joint dust removal parameter and the joint dehumidification parameter, respectively.

In some embodiments of the present disclosure, the accuracy and efficiency of determining the future joint data sequence may be improved by determining the future joint data sequence by the joint determination model; and determining the combined dedusting parameters and the combined dehumidification parameters from the combination of the candidate dedusting parameters and the candidate dehumidification parameters based on the second evaluation score, and selecting the optimal combined operation parameters according to the dust content, the humidity and the power consumption cost, so that the dedusting and dehumidification efficiency and the energy-saving effect are improved.

The embodiments of the present specification also provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the rolled copper foil processing method according to any one of the embodiments of the present specification.

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.

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.

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. The rolled copper foil treatment device is characterized by at least comprising a dust removal module, a dehumidification module, an air filtration module, an environment sensing module, a plasma purging module and a processor:

the environment sensing module is deployed at least one site of a copper foil production workshop and is used for collecting environment data, the environment sensing module comprises at least one of a dust content sensor, a humidity sensor and a temperature sensor, and the environment data comprises at least one of dust content, environment humidity data and environment temperature data;

The processor is configured to:

controlling the air filtering module to filter air which is externally input into the copper foil production workshop;

determining dust removal parameters based on the environmental data, and controlling the dust removal module to operate according to the dust removal parameters so as to remove dust in the copper foil production workshop;

determining a dehumidification parameter based on the environmental data, and controlling the dehumidification module to operate according to the dehumidification parameter so as to remove moisture in the copper foil production workshop;

determining a purging parameter, and controlling the plasma purging module to operate according to the purging parameter so as to remove surface attachments of the copper foil, wherein the purging parameter comprises whether to start the plasma purging device, and at least one of purging operation time and purging operation power after the start;

the determination mode of the purging parameter comprises the following steps:

predicting a dust content index of the dust removal module at a future time point after the dust removal module operates according to the dust removal parameter, and a humidity index of the dehumidification module at the future time point after the dehumidification module operates according to the dehumidification parameter;

starting the plasma purging module in response to the dust content index and/or the humidity index not meeting a preset judging condition;

And determining the purging operation time and/or the purging operation power based on the dust content index and the humidity index in response to starting the plasma purging module.

2. The apparatus of claim 1, further comprising an optical camera, the processor further configured to:

controlling the optical camera to shoot the copper foil to acquire image data;

based on the image data, an image recognition algorithm is used to monitor the surface attachment of the copper foil.

3. The apparatus according to claim 1, wherein the dust removal module comprises at least one negative pressure dust removal apparatus comprising at least a negative pressure blower unit for sucking the air of the copper foil production plant and a filter unit for filtering the sucked air; the at least one negative pressure dust removing device is deployed at the at least one site, and the deployment number and the deployment position of the at least one negative pressure dust removing device are related to the spatial characteristics of the copper foil production workshop.

4. The apparatus of claim 1, wherein the dehumidification module comprises at least one dehumidifier disposed at the at least one site, the number of the at least one dehumidifier disposed and the location of the at least one dehumidifier disposed being related to a space parameter of the copper foil production plant.

5. A method of processing a rolled copper foil, the method performed by a processor, comprising:

controlling an air filtering module to filter air input into a copper foil production workshop from the outside;

determining dust removal parameters based on environmental data, and controlling a dust removal module to operate according to the dust removal parameters so as to remove dust in the copper foil production workshop; the environment data are acquired by an environment sensing module, the environment sensing module comprises at least one of a dust content sensor, a humidity sensor and a temperature sensor, and the environment data comprise at least one of dust content, environment humidity data and environment temperature data;

determining a dehumidification parameter based on the environmental data, and controlling a dehumidification module to operate according to the dehumidification parameter so as to remove moisture in the copper foil production workshop;

determining a purging parameter, and controlling a plasma purging module to operate according to the purging parameter so as to remove surface attachments of the copper foil, wherein the purging parameter comprises whether to start the plasma purging device, and at least one of purging operation time and purging operation power after the start;

the determination mode of the purging parameter comprises the following steps:

and determining the purging operation time and/or the purging operation power based on the dust content index and the humidity index in response to starting a plasma purging module.

6. The method of claim 5, wherein the environmental sensing module further comprises an air flow monitoring device, the environmental data further comprises air change data, and the determining the dedusting parameters based on the environmental data comprises:

determining at least one candidate dust removal parameter;

determining a first evaluation score based on the air change data, the dust content, the space parameters of the copper foil production plant, the candidate dust removal parameters, and a dust content threshold, comprising:

determining a future dust content sequence by a dust removal parameter determination model based on the air change data, the dust content, the space parameter of the copper foil production workshop and the candidate dust removal parameter;

Determining the first evaluation score based on the future dust content sequence, the dust content threshold; the first evaluation score is a quantized value for evaluating the merits of the candidate dust removal parameters;

and taking the candidate dust removal parameter with the minimum first evaluation score as the dust removal parameter.

7. The method of claim 5, wherein the environmental sensing module further comprises an air composition monitor, the environmental data further comprises air composition data, and the determining the dehumidification parameter based on the environmental data comprises:

constructing a dehumidification feature vector based on the air composition data, the ambient temperature data, and the ambient humidity data;

and determining the dehumidification parameters through a dehumidification vector database based on the dehumidification characteristic vector.

8. A computer-readable storage medium storing computer instructions which, when read by a computer in the storage medium, the computer performs the rolled copper foil treatment method according to any one of claims 5 to 7.