CN117317140A - Method for dynamically adjusting pole piece manufacturing procedure based on 5G deterministic network - Google Patents
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- 238000005096 rolling process Methods 0.000 claims abstract description 22
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- 238000005516 engineering process Methods 0.000 claims abstract description 9
- 230000006870 function Effects 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 15
- 238000007581 slurry coating method Methods 0.000 claims description 15
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- 238000001035 drying Methods 0.000 claims description 8
- 238000002360 preparation method Methods 0.000 claims description 8
- 239000002002 slurry Substances 0.000 claims description 8
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 abstract description 7
- 229910001416 lithium ion Inorganic materials 0.000 abstract description 7
- 238000005299 abrasion Methods 0.000 abstract description 2
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- 238000003860 storage Methods 0.000 description 6
- 239000013543 active substance Substances 0.000 description 5
- 239000000853 adhesive Substances 0.000 description 2
- 230000001070 adhesive effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005056 compaction Methods 0.000 description 2
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- 230000002787 reinforcement Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
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- H—ELECTRICITY
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M4/00—Electrodes
- H01M4/02—Electrodes composed of, or comprising, active material
- H01M4/13—Electrodes for accumulators with non-aqueous electrolyte, e.g. for lithium-accumulators; Processes of manufacture thereof
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- G06T7/0004—Industrial image inspection
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
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- G06T2207/30148—Semiconductor; IC; Wafer
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Abstract
The invention relates to the field of manufacturing of lithium ion battery pole pieces, and particularly discloses a method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network, which comprises the steps of collecting production line data in real time; acquiring image information of pole pieces before and after rolling in real time, and processing the image information; extracting characteristic information in production line data and image processing results based on a big data technology, and outputting an evaluation report; and establishing a pole piece manufacturing algorithm model, and carrying out linear, regression and iterative operation based on the model. The invention synthesizes various data in five working procedures, opens up the fence among the working procedures, ensures that the working procedure adjustment is more accurate and more accurate than the manual experience, and improves the yield; meanwhile, the traditional passive parameter adjusting mode is changed, predictive adjustment is performed by using an algorithm, systematic factors affecting quality such as equipment abrasion, raw material difference and the like can be found in advance to a certain extent, parameter optimization adjustment is performed dynamically in real time, batch defective products or unqualified products are avoided, and the operating efficiency of a production line is improved.
Description
Technical Field
The invention relates to the field of manufacturing of lithium ion battery pole pieces, in particular to a method for dynamically adjusting pole piece manufacturing procedures based on a 5G deterministic network.
Background
The pole piece is a key component of the lithium ion battery, the consistency of the thickness of the pole piece has important influence on the capacity, the cycle life, the safety and the like of the power lithium ion battery, and the quality of the pole piece directly influences the quality of the lithium ion battery. The impact on lithium ion battery performance mainly includes: the influence on the energy density of the battery is that the electric quantity of the electrode of the battery is in direct proportion to the mass of the active substance according to Faraday's law, the production process of the pole piece determines the compaction density of the pole piece, and the content of the active substance in the unit volume inside the battery is directly influenced, so that the energy density of the battery is influenced; the influence on the cycle life of the battery is that the adhesive force of the active substance on the current collector can be influenced in the pole piece production process, and the separation and the falling-off of the active substance in the battery charging and discharging process are directly determined by the adhesive force, so that the cycle life of the battery is attenuated due to the separation and the falling-off of the active substance from the current collector; the impact on the internal resistance of the battery, the compaction density of the pole piece affects the pore distribution in the pole piece, thereby affecting the wetting effect and the electron conduction effect of electrolyte in the pole piece, and thus affecting the busy resistance of the battery; the influence on the safety performance of the battery is that the uniformity and the surface roughness of the compacted density of the pole piece are greatly related to lithium precipitation of the negative electrode, copper precipitation of the positive electrode, sharp angle discharge and the like of the battery, so that the safety performance of the battery is further influenced.
The pole piece manufacturing can be divided into five working procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting and pole piece drying. The traditional production line relies on skilled technical workman to carry out the accent according to relevant data and experience, often can appear a certain amount of substandard product or disqualified product, causes certain waste. Especially, three processes of slurry coating, pole piece rolling and pole piece cutting require very high precision, and high yield is difficult to ensure by manpower. Therefore, how to improve the yield and the operation efficiency of the production line is a technical problem of the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide a method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for dynamically tuning pole piece manufacturing processes based on a 5G deterministic network, the method comprising:
acquiring production line data in real time based on a 5G private network; the production line data comprise five working procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting and pole piece drying;
acquiring image information of the pole pieces before and after rolling in real time, and processing the image information to acquire the porosity, aperture distribution and tortuosity of the pole pieces;
extracting characteristic information in production line data and image processing results based on a big data technology, and outputting an evaluation report;
and establishing a pole piece manufacturing algorithm model, and carrying out linear, regression and iterative operation based on the model.
As a further scheme of the invention: the step of collecting production line data in real time based on the 5G private network comprises the following steps:
determining data acquisition equipment according to a production line; the data acquisition equipment is an industrial sensor;
receiving a time delay requirement, a bandwidth requirement and a connection requirement input by a worker, and building a 5G private network based on data acquisition equipment;
and acquiring the production line data acquired by each data acquisition device in real time based on the 5G private network.
As a further scheme of the invention: the detection uplink bandwidth of the image information exceeds 50Mbps, the end-to-end communication time delay is less than 10ms, and the reliability requirement is greater than 99.9999%.
As a further scheme of the invention: the step of extracting characteristic information in production line data and image processing results based on the big data technology and outputting an evaluation report comprises the following steps:
reading various production line data and visual detection results in the pole piece manufacturing process;
inquiring the principle of each process and the association degree of product quality fluctuation in a preset process design database, and determining a data processing model; the data processing model comprises a statistical analysis model and a machine learning model;
and processing and analyzing various production line data and visual detection results based on the data processing model, extracting characteristic information and outputting an evaluation report.
As a further scheme of the invention: the pole piece manufacturing algorithm model is as follows:
wherein,
the algorithm is a second-order Taylor expansion operation based on an objective function;
the objective function is:
the second-order taylor expansion operation formula is as follows:
where l is the loss function, Ω (f t ) For the regular term, constant is a constant term, and f (x) represents a regression tree.
As a further scheme of the invention: the step of establishing a pole piece manufacturing algorithm model and carrying out linear, regression and iterative operation based on the model comprises the following steps:
opening a control channel of the production equipment in real time; the production equipment comprises slurry coating equipment, pole piece rolling equipment and pole piece slitting equipment;
and adjusting the production process based on the control channel.
Compared with the prior art, the invention has the beneficial effects that: the invention synthesizes various data in five working procedures, opens up the fence among the working procedures, ensures that the working procedure adjustment is more accurate and more accurate than the manual experience, and improves the yield; meanwhile, the traditional passive parameter adjusting mode is changed, predictive adjustment is performed by using an algorithm, systematic factors affecting quality such as equipment abrasion, raw material difference and the like can be found in advance to a certain extent, parameter optimization adjustment is performed dynamically in real time, batch defective products or unqualified products are avoided, and the operating efficiency of a production line is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow diagram of a method for dynamically tuning pole piece manufacturing processes based on a 5G deterministic network.
Fig. 2 is a first sub-flowchart of a method for dynamically tuning pole piece manufacturing processes based on a 5G deterministic network.
Fig. 3 is a second sub-flowchart block diagram of a method for dynamically tuning pole piece manufacturing processes based on a 5G deterministic network.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
Fig. 1 is a flow chart of a method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network, in an embodiment of the invention, a method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network includes:
step S100: acquiring production line data in real time based on a 5G private network; the production line data comprise five working procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting and pole piece drying;
step S200: acquiring image information of the pole pieces before and after rolling in real time, and processing the image information to acquire the porosity, aperture distribution and tortuosity of the pole pieces;
step S300: extracting characteristic information in production line data and image processing results based on a big data technology, and outputting an evaluation report;
step S400: establishing a pole piece manufacturing algorithm model, and performing linear, regression and iterative operation based on the model;
the invention is applicable to the field of manufacturing of lithium ion battery pole pieces, and provides a method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network, which comprises the following steps: the 5G deterministic network collects data, ultra-high definition visual detection, big data analysis, algorithm simulation verification and process dynamic optimization;
the 5G deterministic network acquires data, a 5G private network is built in a pole piece manufacturing production line, and the data acquisition of five procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting, pole piece drying and the like of the pole piece manufacturing production line is realized through various industrial sensors and other data acquisition equipment;
the ultra-high definition visual inspection comprises an image acquisition part, an image processing part and a motion control part, wherein the ultra-high definition visual inspection is carried out on pole pieces before and after rolling by using a 5G network through ultra-high definition visual inspection equipment, and the porosity, the aperture distribution and the tortuosity of the pole pieces are detected deeply;
the big data analysis is carried out on various dynamic data and visual detection results in the polar plate manufacturing process, and the big data analysis is carried out by combining the principles of key working procedures, the association of product quality fluctuation and the like;
the algorithm simulation verification is carried out, a pole piece manufacturing algorithm model is established, linear, regression and iterative operation are carried out, deep Reinforcement Learning (DRL) is carried out, and Artificial Intelligence (AI) calculation force is utilized to carry out simulation operation and verification in combination with the principle of key working procedures;
and the process is dynamically optimized, and on the basis of algorithm simulation verification, a control channel of key equipment such as slurry coating, pole piece rolling and pole piece slitting is opened, so that the pole piece manufacturing process is dynamically optimized. Compared with the traditional production line which relies on skilled technicians to perform tuning according to related data and experience.
Fig. 2 is a first sub-flowchart of a method for dynamically adjusting pole piece manufacturing process based on a 5G deterministic network, wherein the step of collecting production line data in real time based on a 5G private network includes:
step S101: determining data acquisition equipment according to a production line; the data acquisition equipment is an industrial sensor;
step S102: receiving a time delay requirement, a bandwidth requirement and a connection requirement input by a worker, and building a 5G private network based on data acquisition equipment;
step S103: and acquiring the production line data acquired by each data acquisition device in real time based on the 5G private network.
The 5G deterministic network acquires data, a predictable, programmable and verifiable private network with deterministic network capacity is built by using a 5G technology and resources, the requirements of an industrial network are met in terms of time delay, bandwidth and multiple connections, and the data acquisition of five procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting, pole piece drying of a pole piece manufacturing production line is realized through various industrial sensors and other data acquisition devices.
As a preferred embodiment of the technical scheme of the invention, the ultra-high definition visual inspection is carried out on the pole pieces before and after rolling by using a 5G network through ultra-high definition visual inspection equipment, and the porosity, aperture distribution and tortuosity of the pole pieces are detected deeply; the ultra-high definition visual inspection system comprises an image acquisition part, an image processing part and a motion control part, and the 5G deterministic network can meet the index requirements of ultra-high definition visual inspection that the uplink bandwidth exceeds 50Mbps, the end-to-end communication time delay is less than 10ms, the reliability requirement is more than 99.9999 percent, and the like.
FIG. 3 is a second sub-flowchart of a method for dynamically tuning pole piece manufacturing processes based on a 5G deterministic network, wherein the steps of extracting characteristic information in production line data and image processing results based on big data technology, and outputting an evaluation report include:
step S301: reading various production line data and visual detection results in the pole piece manufacturing process;
step S302: inquiring the principle of each process and the association degree of product quality fluctuation in a preset process design database, and determining a data processing model; the data processing model comprises a statistical analysis model and a machine learning model;
step S303: and processing and analyzing various production line data and visual detection results based on the data processing model, extracting characteristic information and outputting an evaluation report.
In one example of the technical scheme of the invention, a big data analysis process is defined, and the big data analysis aims at various dynamic data and visual detection results in the pole piece manufacturing process, and by combining the principles of key working procedures, association of product quality fluctuation and the like, the data in the pole piece manufacturing process is processed, calculated, analyzed and valuable information and rules are extracted by utilizing the technical means of statistical analysis, machine learning, signal processing and the like.
The pole piece manufacturing algorithm model is as follows:
wherein,
the algorithm is a second-order Taylor expansion operation based on an objective function;
the objective function is:
the second-order taylor expansion operation formula is as follows:
where l is the loss function, Ω (f t ) For the regular term, constant is a constant term, and f (x) represents a regression tree.
The algorithm input comprises the steps of collecting various data in the five working procedures of pole piece manufacturing, and structural data formed by ultra-high definition vision and quality related parameters after pole piece forming. Each time an input is formed into a tree, feature splitting is continuously carried out to grow, the last predicted residual value is continuously simulated, and a new function is formed. When the input is accumulated to a certain amount, each leaf node corresponds to one score, and finally the scores corresponding to each tree are added up to obtain the predicted value of the sample. The target enables the predicted value of the tree group to be continuously close to the true value, and finally, parameters which need dynamic tuning in the pole piece manufacturing process are obtained.
As a preferred embodiment of the technical scheme of the invention, the step of establishing a pole piece manufacturing algorithm model and performing linear, regression and iterative operations based on the model comprises the following steps:
opening a control channel of the production equipment in real time; the production equipment comprises slurry coating equipment, pole piece rolling equipment and pole piece slitting equipment;
and adjusting the production process based on the control channel.
And the process is dynamically optimized, and on the basis of algorithm simulation verification, a control channel of key equipment of the processes of slurry coating, pole piece rolling, pole piece cutting and the like is opened, so that the pole piece manufacturing process is dynamically optimized.
In order that the above-recited description may be better understood, the invention is further described in connection with embodiments as follows:
as a preferred embodiment of the technical scheme of the invention, the process of collecting data by a 5G deterministic network is described, specifically, the 5G deterministic network comprises a 5G NR set on a production line and an MEC, a return network and a 5G core network set on a machine room, terminal equipment is set on the production line and comprises pressure monitoring, flow rate monitoring, density monitoring, side roller gap monitoring, contact laser monitoring and voltage regulating control equipment, the data of five procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting, pole piece drying and the like of a pole piece manufacturing production line are collected through the equipment such as pressure monitoring, flow rate monitoring, density monitoring, side roller gap monitoring, contact laser monitoring and the like, and the 5G deterministic network is utilized for real-time transmission to a service platform to realize real-time collection of on-site key data of the production line.
As a preferred embodiment of the technical scheme of the invention, the ultra-high definition visual detection process is described, specifically, an ultra-high definition camera is arranged on a production line, ultra-high definition visual detection is carried out on pole pieces before and after rolling, porosity, aperture distribution and tortuosity of the pole pieces are detected deeply, and data of the ultra-high definition visual detection are transmitted to a service platform in real time by utilizing a 5G deterministic network, so that real-time acquisition of key data of the pole pieces on the production line site is realized.
As a preferred embodiment of the technical scheme of the invention, the big data analysis process is described, specifically, a big data analysis system is deployed on a service platform, the data of pressure intensity, flow velocity, density, side roller clearance, contact state and ultra-high definition camera visual detection collected by a terminal device are combined with the principle of key working procedures, the association of product quality fluctuation and the like to carry out big data analysis, and the essential characteristics and development rules between the product quality and five working procedures such as slurry preparation, slurry coating, pole piece rolling, pole piece slitting, pole piece drying and the like are found out.
As a preferred embodiment of the technical scheme of the invention, the algorithm simulation verification is described, specifically, an algorithm simulation verification system is deployed on a service platform, a pole piece manufacturing algorithm model is established, linear, regression and iterative operations are performed, deep reinforcement learning is performed, simulation operation and verification are performed by combining artificial intelligence computing power with the principle of a key process, parameters which need dynamic optimization in the pole piece manufacturing process are obtained, and continuous iterative optimization is performed in the running process of a production line.
As a preferred embodiment of the technical scheme of the invention, the dynamic process optimization is described, specifically, a dynamic process optimization control system is deployed on a service platform, and on the basis of algorithm simulation verification, the control channels of key equipment such as slurry coating, pole piece rolling and pole piece slitting are opened, and the dynamic process optimization of pole piece manufacturing is performed through a voltage regulation control device in terminal equipment.
The method for dynamically adjusting the pole piece manufacturing procedure based on the 5G deterministic network can achieve the functions achieved by the method, and the computer equipment comprises one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the method for dynamically adjusting the pole piece manufacturing procedure based on the 5G deterministic network.
The processor takes out instructions from the memory one by one, analyzes the instructions, then completes corresponding operation according to the instruction requirement, generates a series of control commands, enables all parts of the computer to automatically, continuously and cooperatively act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
For example, a computer program may be split into one or more modules, one or more modules stored in memory and executed by a processor to perform the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
It will be appreciated by those skilled in the art that the foregoing description of the service device is merely an example and is not meant to be limiting, and may include more or fewer components than the foregoing description, or may combine certain components, or different components, such as may include input-output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device described above, and which connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used for storing computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template display function, a product information release function, etc.), and the like; the storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The modules/units integrated in the terminal device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may implement all or part of the modules/units in the system of the above-described embodiments, or may be implemented by instructing the relevant hardware by a computer program, which may be stored in a computer-readable storage medium, and which, when executed by a processor, may implement the functions of the respective system embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. A method for dynamically adjusting pole piece manufacturing procedures based on a 5G deterministic network, the method comprising:
acquiring production line data in real time based on a 5G private network; the production line data comprise five working procedures of slurry preparation, slurry coating, pole piece rolling, pole piece slitting and pole piece drying;
acquiring image information of the pole pieces before and after rolling in real time, and processing the image information to acquire the porosity, aperture distribution and tortuosity of the pole pieces;
extracting characteristic information in production line data and image processing results based on a big data technology, and outputting an evaluation report;
and establishing a pole piece manufacturing algorithm model, and carrying out linear, regression and iterative operation based on the model.
2. The method for dynamically adjusting and optimizing pole piece manufacturing procedures based on a 5G deterministic network according to claim 1, wherein the step of collecting production line data in real time based on a 5G private network comprises:
determining data acquisition equipment according to a production line; the data acquisition equipment is an industrial sensor;
receiving a time delay requirement, a bandwidth requirement and a connection requirement input by a worker, and building a 5G private network based on data acquisition equipment;
and acquiring the production line data acquired by each data acquisition device in real time based on the 5G private network.
3. The method for dynamically adjusting and optimizing pole piece manufacturing procedures based on 5G deterministic network according to claim 1, wherein the detection uplink bandwidth of the image information exceeds 50Mbps, the end-to-end communication time delay is less than 10ms, and the reliability requirement is greater than 99.9999%.
4. The method for dynamically adjusting pole piece manufacturing process based on 5G deterministic network according to claim 1, wherein the step of extracting characteristic information in production line data and image processing results based on big data technology and outputting an evaluation report comprises:
reading various production line data and visual detection results in the pole piece manufacturing process;
inquiring the principle of each process and the association degree of product quality fluctuation in a preset process design database, and determining a data processing model; the data processing model comprises a statistical analysis model and a machine learning model;
and processing and analyzing various production line data and visual detection results based on the data processing model, extracting characteristic information and outputting an evaluation report.
5. The method for dynamically tuning pole piece manufacturing process based on 5G deterministic network according to claim 1, wherein the pole piece manufacturing algorithm model is:
wherein,
the algorithm is a second-order Taylor expansion operation based on an objective function;
the objective function is:
the second-order taylor expansion operation formula is as follows:
where l is the loss function, Ω (f t ) For the regular term, constant is a constant term, and f (x) represents a regression tree.
6. The method for dynamically tuning a pole piece manufacturing process based on a 5G deterministic network according to claim 1, wherein the step of establishing a pole piece manufacturing algorithm model and performing linear, regression and iterative operations based on the model comprises:
opening a control channel of the production equipment in real time; the production equipment comprises slurry coating equipment, pole piece rolling equipment and pole piece slitting equipment;
and adjusting the production process based on the control channel.
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| CN118011780B (en) * | 2024-04-08 | 2024-06-11 | 钛玛科（北京）工业科技有限公司 | Control method and system of lithium battery roll slitting machine based on PID |
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