A green production engineering construction method and system of a graphene-based electrode material

Abstract

The application provides a green production engineering construction method and system of a graphene-based electrode material, relates to the technical field of graphene-based electrode materials, and comprises the following steps: determining a production process flow; continuously counting product production data in a production process; acquiring production environment data of a product production workshop; determining a process production green parameter for measuring the green degree of the production process according to the product production data and the production environment data; determining a process parameter controllable ability for representing process stability according to the production process flow and the product production data; determining an optimal production process flow according to the process production green parameter and the process parameter controllable ability; and formulating and executing a corresponding graphene-based electrode material green production engineering construction scheme according to the optimal production process flow, and dynamically optimizing a production running state. According to the application, the comprehensiveness of the green production engineering construction method of the graphene-based electrode material can be improved.

Description

Technical Field

[0001] This invention relates to the field of graphene-based electrode materials technology, and in particular to a green production engineering construction method and system for graphene-based electrode materials. Background Technology

[0002] In related technologies, existing production methods often focus only on a single indicator and lack a method that can synergistically quantify and analyze the degree of greening of production and process stability. That is, related technologies have difficulty in screening out the production process flow that performs optimally in terms of both green environmental protection and stable controllability, and it is difficult to build a comprehensive green evaluation and process controllability model, and it is difficult to improve the comprehensiveness of green production engineering construction methods for graphene-based electrode materials.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a green production engineering construction method and system for graphene-based electrode materials, which can solve the technical problems of related technologies that make it difficult to select the production process that performs optimally in terms of both green environmental protection and stability and controllability, difficult to construct a comprehensive green evaluation and process controllability model, and difficult to improve the comprehensiveness of the green production engineering construction method for graphene-based electrode materials.

[0005] According to a first aspect of the present invention, a green production engineering construction method for graphene-based electrode materials is provided, comprising:

[0006] The process steps for obtaining graphene-based electrode materials are defined, and the production process flow is determined based on the process steps.

[0007] During the production of graphene-based electrode materials, product production data is continuously collected and integrated into the manufacturing execution system dashboard.

[0008] Environmental sensors are deployed on the graphene-based electrode material production line to acquire production environment data from the product manufacturing workshop;

[0009] Based on the product production data and the production environment data, determine the green production parameters for measuring the degree of greening of the production process;

[0010] Based on the production process flow and the product production data, determine the controllability of process parameters that represent process stability;

[0011] The optimal production process flow is determined based on the green production parameters of the process and the controllability of the process parameters.

[0012] Based on the optimal production process, a corresponding green production engineering construction plan for graphene-based electrode materials was formulated and implemented, and the production operation status was dynamically optimized.

[0013] According to the present invention, the process steps for obtaining graphene-based electrode materials, and the process flow determined according to the process steps, include:

[0014] The process steps for obtaining graphene-based electrode materials include: raw material pretreatment process, material modification and integration process, and resource recycling process.

[0015] For the aforementioned production steps, multiple interchangeable alternative production process schemes are generated;

[0016] A preliminary evaluation of the proposed production process options is conducted, and a set of options that meet the constraints of production efficiency and cost are selected and arranged into a production process flow.

[0017] According to the present invention, based on the product production data and the production environment data, process greening parameters for measuring the degree of greening of the production process are determined, including:

[0018] Based on the product production data, obtain the product qualification rate, unit time production capacity, unit time product energy consumption, and unit time product waste.

[0019] Based on the production environment data, obtain the actual environmental factor values ​​that characterize the workshop environment.

[0020] Obtain environmental factor thresholds, acceptable ranges for environmental factors, and the power of environmental control systems;

[0021] Based on the product qualification rate, the unit time production capacity, the unit time product energy consumption, the unit time product waste, the actual environmental factor value, the environmental factor threshold, the environmental factor qualification range, and the environmental control system power, the process production green parameters that measure the degree of greening of the production process are calculated and determined through a composite function model.

[0022] According to the present invention, based on the product qualification rate, the unit time production capacity, the unit time product energy consumption, the unit time product waste, the actual environmental factor value, the environmental factor threshold, the environmental factor acceptable range, and the environmental control system power, process production green parameters measuring the degree of greening of the production process are calculated and determined through a composite function model, including: according to the formula: ,

[0023] Determine the green production parameters for the i-th process step. ,in, Let be the product qualification rate of the i-th process step. Let be the unit time capacity of the i-th process step. Let i be the product energy consumption per unit time for the i-th process step. Let i be the amount of product waste per unit time in the i-th process step. For the preset unit time length, For the power of the environmental control system, Let j be the actual environmental factor value. Let the threshold be the j-th environmental factor. For the j-th environmental factor, the acceptable range is... Let J be the preset waste energy consumption coefficient, j≤J, where J is the number of environmental factors, and both j and J are positive integers.

[0024] According to the present invention, based on the production process flow and the product production data, determining the controllability of process parameters representing process stability includes:

[0025] Based on the aforementioned production process flow, identify and define the key process nodes that have a critical impact on the final product quality and process stability.

[0026] Based on the key process nodes and the product production data, determine the maximum value of the change in key process parameters, the minimum value of the change in key process parameters, the allowable fluctuation range of the change in key process parameters, the target value of key process parameters, the average value of key process parameters, and the deviation range of key process parameters;

[0027] The fluctuation level of the key process parameter is determined based on the maximum value of the change in the key process parameter, the minimum value of the change in the key process parameter, and the allowable fluctuation range of the change in the key process parameter.

[0028] The deviation level of the key process parameters is determined based on the target quantity of the key process parameters, the average value of the key process parameters, and the deviation range of the key process parameters.

[0029] Based on the fluctuation level and deviation level of the key process parameters, the controllability of process parameters representing process stability is determined.

[0030] According to the present invention, determining the controllability of process parameters representing process stability based on the fluctuation level and deviation level of the key process parameters includes:

[0031] When the condition that the fluctuation of the change in the key process parameter is less than or equal to the allowable fluctuation range of the change in the key process parameter is met, the fluctuation level of the key process parameter is 1 minus the relative difference between the fluctuation of the change in the key process parameter and the allowable fluctuation range of the change in the key process parameter.

[0032] When the fluctuation of the change in the key process parameter is greater than the allowable fluctuation range of the change in the key process parameter, the fluctuation level of the key process parameter is 0.

[0033] When the condition that the average deviation of the key process parameter is less than or equal to the deviation range of the key process parameter is met, the deviation level of the key process parameter is 1 minus the relative difference between the average deviation of the key process parameter and the deviation range of the key process parameter.

[0034] When the average deviation of the key process parameter is greater than the deviation range of the key process parameter, the deviation level of the key process parameter is 0.

[0035] The controllability of process parameters representing process stability is determined by the arithmetic mean of the fluctuation levels of key process parameters at each critical node and the product of the deviation levels of key process parameters at each critical node.

[0036] According to the present invention, an optimal production process flow is determined based on the green production parameters and the controllability of the process parameters, including:

[0037] Based on the green production parameters of the process and the controllability of the process parameters, determine the standards for green production parameters and the standards for controllability of process parameters.

[0038] Calculate the green production parameters of each alternative process scheme under each process, compare them with the green production parameter standards, and thus determine multiple candidate production process flows that meet the green requirements.

[0039] Calculate the controllability of process parameters for each candidate production process, compare it with the controllability standard of process parameters, and determine the final production process that performs best in terms of greenness and controllability as the optimal production process.

[0040] According to the present invention, based on the optimal production process flow, a corresponding green production engineering construction plan for graphene-based electrode materials is formulated and implemented, and the production operation status is dynamically optimized, including:

[0041] Based on the green production parameters of the process and the controllability of the process parameters, the production operation status is determined, wherein the production operation status includes: green production balance status and green production imbalance status.

[0042] Based on the optimal production process, the probability of a green imbalance state in production is predicted, and based on the predicted imbalance state probability and the production operation status, a green production engineering construction plan for graphene-based electrode materials is determined.

[0043] When the conditions for production operation to be in a state of green balance are met, a dynamic optimization instruction to maximize output is triggered.

[0044] When the conditions for production operation to be in a state of green imbalance are met, a dynamic optimization instruction to reduce speed and stabilize is triggered.

[0045] According to the present invention, based on the optimal production process flow, a predicted imbalance state probability of occurrence in the production process is predicted, and based on the predicted imbalance state probability and the production operation status, a green production engineering construction scheme for graphene-based electrode materials is determined, including:

[0046] Based on the green balance and imbalance states of production at multiple moments in the k-th production stage, determine the probability of the actual imbalance state in the k-th production stage.

[0047] When k equals 0, the construction plan for the green production of graphene-based electrode materials in the (k+1)th production stage is determined based on the predicted imbalance probability of the (k+1)th production stage.

[0048] When k is greater than or equal to 1, the comprehensive predicted imbalance probability is determined based on the predicted imbalance probability of the (k+1)th production stage and the actual imbalance probability of the kth production stage, and the construction plan for the green production project of graphene-based electrode materials in the (k+1)th production stage is determined based on the comprehensive predicted imbalance probability.

[0049] According to a second aspect of the present invention, a green production engineering construction system for graphene-based electrode materials is provided, comprising:

[0050] The process flow module is used to obtain the process production steps of graphene-based electrode materials and determine the production process flow based on the process production steps.

[0051] The production data module is used to continuously collect product production data during the production process of graphene-based electrode materials and integrate the collected product production data into the manufacturing execution system dashboard.

[0052] The environmental data module is used to deploy environmental sensors on the graphene-based electrode material production line to acquire production environment data of the product manufacturing workshop.

[0053] The green parameter module is used to determine process production green parameters that measure the degree of greening of the production process based on the product production data and the production environment data.

[0054] The controllability module is used to determine the controllability of process parameters representing process stability based on the production process flow and the product production data.

[0055] The optimal process module is used to determine the optimal production process flow based on the green production parameters of the process and the controllability of the process parameters.

[0056] The engineering construction module is used to formulate and implement corresponding green production engineering construction plans for graphene-based electrode materials based on the optimal production process flow, and to dynamically optimize the production operation status.

[0057] Technical Effects: According to the present invention, the process production steps of graphene-based electrode materials can be obtained, and the production process flow can be determined based on the process production steps. Product production data and production environment data of the production workshop can be obtained during the production process. Based on the product production data and production environment data, green parameters for measuring the degree of greenness of the production process can be determined. Simultaneously, based on the production process flow and product production data, the controllability of process parameters representing process stability can be determined. Furthermore, based on the green parameters and controllability of process parameters, the optimal production process flow can be determined. Finally, based on the optimal production process flow, a corresponding green production engineering construction plan for graphene-based electrode materials can be formulated and implemented, and the production operation status can be dynamically optimized, thus improving the comprehensiveness of the green production engineering construction method for graphene-based electrode materials. When determining the green parameters of the production process, the green parameters of the production process can be calculated using a composite function model based on the product qualification rate, unit time capacity, unit time product energy consumption, unit time product waste, actual environmental factor values, environmental factor thresholds, environmental factor compliance ranges, and environmental control system power. In the calculation process, the contribution of the green efficiency of output, environmental control energy consumption, and environmental factor compliance to the green parameters of the production process is comprehensively considered, which improves the accuracy of the green parameters of the production process.

[0058] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0060] Figure 1 An exemplary flowchart illustrates a green production engineering construction method for graphene-based electrode materials according to an embodiment of the present invention;

[0061] Figure 2 A schematic diagram illustrating the determination of a production process flow according to an embodiment of the present invention is shown exemplarily;

[0062] Figure 3 A schematic diagram illustrating the determination of green production process parameters according to an embodiment of the present invention is shown;

[0063] Figure 4 A schematic diagram illustrating the controllability of process parameters according to an embodiment of the present invention is shown exemplarily.

[0064] Figure 5 A schematic diagram illustrating the determination of the optimal production process flow according to an embodiment of the present invention is shown exemplarily;

[0065] Figure 6 An exemplary schematic diagram of dynamic optimization of production operation status according to an embodiment of the present invention is shown;

[0066] Figure 7 A block diagram of a green production engineering construction system for graphene-based electrode materials according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0069] Figure 1 An exemplary flowchart illustrates a green production engineering construction method for graphene-based electrode materials according to an embodiment of the present invention, the method comprising:

[0070] Step S1: Obtain the process production steps of the graphene-based electrode material, and determine the production process flow based on the process production steps;

[0071] Step S2: During the production process of graphene-based electrode materials, continuously collect product production data during the production process, and integrate the collected product production data into the manufacturing execution system dashboard;

[0072] Step S3: Deploy environmental sensors on the graphene-based electrode material production line to acquire production environment data of the product manufacturing workshop.

[0073] Step S4: Based on the product production data and the production environment data, determine the green production parameters for measuring the degree of greening of the production process;

[0074] Step S5: Based on the production process flow and the product production data, determine the controllability of process parameters that represent process stability;

[0075] Step S6: Determine the optimal production process flow based on the green production parameters and the controllability of the process parameters.

[0076] Step S7: Based on the optimal production process, formulate and implement a corresponding green production engineering construction plan for graphene-based electrode materials, and dynamically optimize the production operation status.

[0077] The green production engineering construction method for graphene-based electrode materials according to embodiments of the present invention can obtain the process production steps of graphene-based electrode materials, determine the production process flow based on the process production steps, and obtain product production data and production environment data of the product production workshop during the production process. Based on the product production data and production environment data, process production green parameters that measure the degree of greening of the production process are determined. At the same time, based on the production process flow and product production data, the controllability of process parameters that represent process stability is determined. Furthermore, based on the process production green parameters and the controllability of process parameters, the optimal production process flow is determined. Finally, based on the optimal production process flow, a corresponding green production engineering construction plan for graphene-based electrode materials is formulated and implemented, and the production operation status is dynamically optimized, thereby improving the comprehensiveness of the green production engineering construction method for graphene-based electrode materials.

[0078] According to an embodiment of the present invention, in step S1, the process production steps of the graphene-based electrode material are obtained, and the production process flow is determined according to the process production steps.

[0079] Figure 2 A schematic diagram illustrating the determination of a production process flow according to an embodiment of the present invention is shown as an example.

[0080] According to an embodiment of the present invention, step S1 includes:

[0081] Step S11, obtaining the process production steps of graphene-based electrode material, wherein the process production steps include: raw material pretreatment process, material modification and integration process and resource recycling process;

[0082] Step S12: For the aforementioned production process steps, generate multiple interchangeable alternative production process schemes.

[0083] Step S13: Conduct a preliminary evaluation of the alternative production processes, select a set of alternatives that meet the constraints of production efficiency and cost, and arrange them into a production process flow.

[0084] For example, the production process of graphene-based electrode materials refers to the steps in the production of the main products during the production of graphene-based electrode materials. The raw material pretreatment process is responsible for the green exfoliation or catalytic synthesis of graphite raw materials. The material modification and integration process is responsible for the doping, coating, or compounding of graphene with active materials (such as silicon, sulfur, and metal oxides). The resource recycling process is responsible for integrating the recovery of waste heat and waste gas (such as CO and CH4), wastewater treatment, and solvent reuse during the production process. For each production process step, at least two alternative schemes are designed for screening. For example, the raw material pretreatment process can adopt an electrochemical exfoliation process or a supercritical CO2-assisted exfoliation process. From the perspectives of the feasibility of large-scale preparation and the continuity of the production process, a set of alternative schemes that meet the constraints of production efficiency and cost are selected. This facilitates the subsequent design of different production process flows based on the set of alternative schemes, and the selection of the best one for production.

[0085] According to one embodiment of the present invention, in step S2, during the production process of graphene-based electrode materials, product production data during the production process is continuously collected and integrated into the manufacturing execution system dashboard.

[0086] For example, product production data can be obtained through online testing equipment and manual statistics, and the collected product production data can be integrated into the manufacturing execution system dashboard to facilitate observation of the real-time production status of the product.

[0087] According to one embodiment of the present invention, in step S3, environmental sensors are arranged on the graphene-based electrode material production line to acquire production environment data of the product manufacturing workshop.

[0088] For example, environmental sensors such as thermometers, hygrometers, and barometers can be used to obtain production environment data from the product manufacturing workshop.

[0089] According to an embodiment of the present invention, in step S4, process production green parameters for measuring the degree of greening of the production process are determined based on the product production data and the production environment data.

[0090] Figure 3 A schematic diagram illustrating the determination of green production process parameters according to an embodiment of the present invention is shown.

[0091] According to an embodiment of the present invention, step S4 includes:

[0092] Step S41: Based on the product production data, obtain the product qualification rate, unit time production capacity, unit time product energy consumption, and unit time product waste amount;

[0093] Step S42: Based on the production environment data, obtain the actual environmental factor values ​​that characterize the workshop environment.

[0094] Step S43: Obtain environmental factor thresholds, acceptable ranges for environmental factors, and power of the environmental control system;

[0095] Step S44: Based on the product qualification rate, the unit time production capacity, the unit time product energy consumption, the unit time product waste amount, the actual environmental factor value, the environmental factor threshold, the environmental factor qualified range, and the environmental control system power, calculate and determine the process production green parameters that measure the degree of greening of the production process through a composite function model.

[0096] For example, the product qualification rate is used to measure the proportion of qualified products produced by a particular process out of the total output. Qualified products are detected by online inspection equipment on the production line. The calculation method determines the product qualification rate and integrates it into the Manufacturing Execution System (MES) dashboard. Unit-time capacity refers to the number of products that the process can produce within a unit of time (each process step in the preparation of graphene-based electrode materials requires a certain reaction time; typically, a unit of time is preset to one shift (8 hours). All unit times below are set to one shift, maintaining consistency throughout the system). This reflects the production efficiency of the process and can be manually tallied and integrated into the MES dashboard. Unit-time product energy consumption refers to the amount of energy consumed by the products produced within a unit of time, reflecting the energy efficiency of the process. Meters such as electricity meters, steam meters, or gas meters are installed on the production equipment (e.g., reactors, drying ovens) to display equipment energy consumption, obtaining real-time energy consumption data. This data is then calculated and converted into unit-time product energy consumption and integrated into the MES dashboard. Unit-time product waste refers to the amount of waste generated during the product production process within a unit of time. The total amount of waste materials, scraps, waste liquid, waste gas, etc. (mass or volume, where mass is in kilograms and volume is in cubic meters. When waste includes both gaseous and solid forms, the amount of product waste per unit time is the sum of the amounts of gaseous and solid waste. For example, when gaseous and solid waste are 1 kilogram and 1 cubic meter respectively, the amount of product waste per unit time is 2, and the amount of product waste per unit time is a dimensionless value), reflects the resource utilization rate and pollution control level of the process. It is statistically analyzed using weighing equipment (solid waste), flow meters (waste liquid), and waste gas monitoring instruments (VOCs). Actual environmental factor values ​​refer to the actual measured values ​​of environmental parameters that affect product output in the product production workshop, reflecting the true state of the product production environment. For example, ambient temperature and humidity can be collected by temperature and humidity meters, and air pressure intensity can be collected by air pressure sensors. Each collected environmental index is an actual environmental factor value.Environmental factor thresholds refer to ideal or standard environmental factor values ​​set to ensure product quality. Actual environmental factor values ​​deviating from these thresholds can negatively impact product quality. These thresholds are determined through process specifications, safety regulations, or industry standards and are recorded in production control procedures or process cards. For example, the environmental temperature threshold might be 25℃, and the environmental humidity threshold might be 40% RH. The acceptable range for environmental factors refers to the reasonable variation range of actual environmental factor values ​​centered on the environmental factor threshold. For instance, temperature fluctuations exceeding ±5℃ may lead to uneven graphene layer counts; the allowable temperature fluctuation range can be set to 5. Humidity fluctuations exceeding ±8% RH will cause slow drying of graphene slurry (too high humidity) and the generation of graphene dust (too low humidity); the allowable humidity fluctuation range can be set to 8. Air pressure fluctuations exceeding ±10 Pa will cause graphene oxidation (too high pressure) and external environmental pollution (too low pressure). The environmental control system power is set through process sensitivity, product quality control standards, or environmental safety regulations, such as ISO 14644 (cleanroom) and GB / T 18883 (indoor air quality). The power of the environmental control system refers to the total power consumed by auxiliary systems to maintain a stable production environment (e.g., temperature, humidity, cleanliness, airflow), such as the power of air conditioning, purification systems, or exhaust systems. This is achieved by installing an energy monitoring instrument in the main circuit of the environmental control system to record the average operating power. Based on product qualification rate, unit-time production capacity, unit-time product energy consumption, unit-time product waste volume, actual environmental factor values, environmental factor thresholds, environmental factor acceptable ranges, and the power of the environmental control system, the contribution of production energy consumption, environmental energy consumption, and environmental factor interference is comprehensively evaluated. A composite function model is used to calculate and determine the process greening parameters that measure the degree of greening of the production process.

[0097] According to an embodiment of the present invention, step S44 includes: determining the green production parameters of the i-th process step according to formula (1). , (1),

[0098] in, Let be the product qualification rate of the i-th process step. Let be the unit time capacity of the i-th process step. Let i be the product energy consumption per unit time for the i-th process step. Let i be the amount of product waste per unit time in the i-th process step. For the preset unit time length, For the power of the environmental control system, Let j be the actual environmental factor value. Let the threshold be the j-th environmental factor. For the j-th environmental factor, the acceptable range is... Let J be the preset waste energy consumption coefficient, j≤J, where J is the number of environmental factors, and both j and J are positive integers.

[0099] According to one embodiment of the present invention, To produce green efficiency items, Let be the product qualification rate of the i-th process step. Let be the unit time capacity of the i-th process step. Let be the product of the product qualification rate of the i-th process step and the output capacity per unit time, representing the number of qualified products produced by the i-th process step per unit time. The larger the item, the more qualified products are produced. The smaller the item, the fewer qualified products are produced. The amount of product waste per unit time. To preset the energy consumption coefficient for waste, This represents the energy consumption required to process product waste generated per unit time. The preset waste energy consumption coefficient can be calculated experimentally, and the energy consumption for processing product waste per unit time is used as the preset waste energy consumption coefficient. Let i be the product energy consumption per unit time for the i-th process step. Let be the sum of the energy consumption per unit time for product processing and the energy consumption per unit time for waste disposal in the i-th process step, representing the total energy consumption required to produce the product per unit time in the i-th process step. The larger the value, the greater the total energy consumption required to produce the product per unit time. The smaller the value, the lower the total energy consumption required to produce products per unit of time. Let be the ratio of the number of qualified products produced per unit time in the i-th process step to the total energy consumption required to produce products per unit time. This is a dimensionless value, representing the efficiency of producing qualified products by consuming the total energy required per unit time for the i-th process step. The larger the item, The smaller the item, The larger the value, the higher the efficiency of the i-th process step in producing qualified products per unit time, and the higher the degree of greenness of the process. The smaller the item, The larger the item, The smaller the value of the term, the lower the efficiency of producing qualified products by consuming the total energy required per unit time for the i-th process step, and the lower the degree of greenness of the process.

[0100] According to one embodiment of the present invention, For energy consumption related to environmental regulation, For a preset unit time length, such as, It can be set to 8 hours. For the power of the environmental control system, This is the product of the power of the environmental control system and the preset unit time length, representing the total energy consumption consumed per unit time to maintain the stability of the process environment. The larger, The larger the value, the greater the total energy consumption per unit time to maintain a stable process environment. The smaller, The smaller the value, the less total energy is consumed per unit time to maintain a stable process environment. It is the ratio of the total energy consumed to maintain a stable process environment per unit time to the number of qualified products produced per unit time. This is a dimensionless value, representing the environmental control energy consumption required to produce qualified products per unit time. The larger, The larger the value, the greater the environmental energy consumption for producing qualified products per unit time. The smaller the size, the lower the degree of greenness of the process. The smaller, The smaller the value, the less environmental control energy is required per unit of time to produce qualified products. The larger the size, the more environmentally friendly the process.

[0101] According to one embodiment of the present invention, For environmental factors, the following items are acceptable. Let j be the actual environmental factor value. Let the threshold be the j-th environmental factor. Let be the absolute value of the difference between the j-th actual environmental factor value and the j-th environmental factor threshold, representing the deviation of the j-th actual environmental factor value. The closer the j-th actual environmental factor value is to the j-th environmental factor threshold, the greater the deviation. The smaller the value, the smaller the deviation of the j-th actual environmental factor value, and the greater the deviation of the j-th actual environmental factor value from the j-th environmental factor threshold. The larger the value, the greater the deviation of the j-th actual environmental factor value. For the j-th environmental factor, the acceptable range is... The value of the deviation of the j-th actual environmental factor value to the acceptable range of the j-th environmental factor represents the degree of deviation of the j-th actual environmental factor value. The smaller the value, the smaller the deviation of the j-th actual environmental factor value. The larger the value, the less negative impact environmental factors have on product quality; the larger the environmental compliance factor, the higher the degree of greenness of the process. The larger the value, the greater the deviation of the j-th actual environmental factor value. The smaller the value, the greater the negative impact of environmental factors on product quality; the smaller the environmental compliance factor, the lower the degree of greenness of the process.

[0102] According to one embodiment of the present invention, The item represents the output green efficiency item. Environmental control energy consumption item and environmental factor compliance items Under the combined influence of various factors, the larger the values ​​of each factor, the better the green production parameters of the i-th process step. The larger the value, the greater the degree of greenness in the production process of the i-th process step.

[0103] In this way, based on product qualification rate, unit time production capacity, unit time product energy consumption, unit time product waste, actual environmental factor values, environmental factor thresholds, environmental factor compliance ranges, and environmental control system power, the green parameters of the production process can be calculated and determined through a composite function model to measure the degree of greening of the production process. In the calculation process, the contribution of the green efficiency of output, the energy consumption of environmental control, and the compliance of environmental factors to the green parameters of the production process is comprehensively considered, thereby improving the accuracy of the green parameters of the production process.

[0104] According to one embodiment of the present invention, in step S5, the controllability of process parameters representing process stability is determined based on the production process flow and the product production data.

[0105] Figure 4 A schematic diagram illustrating the controllability of process parameters according to an embodiment of the present invention is shown as an example.

[0106] According to an embodiment of the present invention, step S5 includes:

[0107] Step S51: Based on the production process flow, identify and define the key process nodes that have a critical impact on the final product quality and process stability.

[0108] Step S52: Based on the key process nodes and the product production data, determine the maximum value of the change in key process parameters, the minimum value of the change in key process parameters, the allowable fluctuation range of the change in key process parameters, the target value of key process parameters, the average value of key process parameters, and the deviation range of key process parameters.

[0109] Step S53: Determine the fluctuation level of the key process parameter based on the maximum value of the change in the key process parameter, the minimum value of the change in the key process parameter, and the allowable fluctuation range of the change in the key process parameter.

[0110] Step S54: Determine the deviation level of the key process parameters based on the target quantity of the key process parameters, the average value of the key process parameters, and the deviation range of the key process parameters;

[0111] Step S55: Based on the fluctuation level of the key process parameters and the deviation level of the key process parameters, determine the controllability of the process parameters that represent process stability.

[0112] For example, critical process nodes refer to different production nodes in the production process that affect the quality of the output product and the stability of the process. Examples include nodes such as peeling, purification, and drying in the raw material pretreatment process, and nodes such as compounding, annealing, and pulverizing in the material modification and integration process. Critical process parameters refer to the core intrinsic parameters corresponding to each critical node, which directly reflect the performance of the output product. Examples include the peeling rate at the peeling node, the residual metal impurity rate at the purification node, and the residual moisture rate at the drying node. The change in critical process parameters refers to the magnitude of change of the critical process parameter within a unit of time; the change in critical process parameters is a vector quantity. The maximum value of a critical process parameter variation is the maximum value of the variation amplitude of the critical process parameter over a few recent time units, and the minimum value is the minimum value of the variation amplitude of the critical process parameter over a few recent time units. The maximum and minimum values ​​can be determined by statistically analyzing the variation amplitude of the critical process parameter over a certain number of time units based on the production time, such as ten time units. The average value of a critical process parameter refers to the average value of the critical process parameter within one unit of time. By statistically analyzing the variation of the critical process parameter for each unit of time, the variation values ​​are integrated into the Manufacturing Execution System (MES) dashboard. Statistical functions are used to calculate the maximum and minimum values ​​of the critical process parameter variation and the average value. The allowable fluctuation range of the critical process parameter variation refers to the permissible variation amplitude of the process parameter variation at this critical node. The target value of the critical process parameter refers to the design value of the process parameter at this critical node, i.e., the level that the process parameter needs to reach to achieve ideal product quality and process stability. The deviation range of the critical process parameter refers to the reasonable variation amplitude of the process parameter at this critical node centered on the target value of the critical process parameter. The allowable fluctuation range, target value, and deviation range of the critical process parameter variation can be obtained from process design documents (e.g., SOPs, process control procedures). Extracted from specifications and design drawings; based on the maximum value, minimum value, and allowable fluctuation range of key process parameters, assess the stability of individual key process parameters and determine their fluctuation levels; based on the target value, average value, and deviation range of key process parameters, assess the average deviation stability of all key process parameters and determine their deviation levels; based on the fluctuation and deviation levels of key process parameters, comprehensively evaluate the impact of the fluctuation and average deviation of key process parameters to determine the controllability of process parameters representing process stability.

[0113] According to an embodiment of the present invention, step S55 includes:

[0114] Step S551: When the condition that the fluctuation of the change in the key process parameter is less than or equal to the allowable fluctuation range of the change in the key process parameter is met, the fluctuation level of the key process parameter is 1 minus the relative difference between the fluctuation of the change in the key process parameter and the allowable fluctuation range of the change in the key process parameter.

[0115] Step S552: When the condition that the fluctuation of the change in the key process parameter is greater than the allowable fluctuation range of the change in the key process parameter is met, the fluctuation level of the key process parameter is 0.

[0116] Step S553: ​​When the condition that the average deviation of the key process parameter is less than or equal to the deviation range of the key process parameter is met, the deviation level of the key process parameter is 1 minus the relative difference between the average deviation of the key process parameter and the deviation range of the key process parameter.

[0117] Step S554: When the condition that the average deviation of the key process parameter is greater than the deviation range of the key process parameter is met, the deviation level of the key process parameter is 0.

[0118] Step S555: Based on the arithmetic mean of the fluctuation level of the key process parameters at each key node and the product of the deviation levels of the key process parameters at each key node, determine the controllability of the process parameters that represent process stability.

[0119] For example, the fluctuation level of the critical process parameter at the nth critical process node can be determined based on the maximum value, minimum value, and allowable fluctuation range of the critical process parameter. Including: according to the formula Determine the fluctuation level of the key process parameters at the nth critical process node. ,in, This represents the maximum change in the critical process parameter at the nth critical process node. This represents the minimum change in the critical process parameter at the nth critical process node. This represents the allowable fluctuation range of the critical process parameter for the nth critical process node, where n ≤ N, N is the number of critical nodes, and both n and N are positive integers. The difference between the maximum and minimum values ​​of the change in the key process parameter represents the fluctuation of the change in the key process parameter. This is true when the following conditions are met: When the condition is met, that is, when the fluctuation of the change in the key process parameter is less than or equal to the allowable fluctuation range of the change in the key process parameter, This is the ratio of the fluctuation range of the key process parameter to the allowable fluctuation range of the key process parameter, indicating the degree of fluctuation in the key process parameter. The smaller the value, the smaller the fluctuation of the key process parameter; the greater the fluctuation level of the key process parameter (the greater the fluctuation level of the key process parameter, the more stable it is); and the better the stability of the key process parameter fluctuation. The larger the value, the greater the fluctuation of the key process parameter; the smaller the fluctuation level of the key process parameter (the smaller the fluctuation level of the key process parameter, the less unstable it is), the worse the stability of the key process parameter fluctuation. When the condition is met, that is, when the fluctuation of the change in the key process parameter exceeds the allowable fluctuation range of the change in the key process parameter, the fluctuation range exceeds the upper limit of the production conditions, which can be regarded as the key process parameter of the production process being out of control, and the fluctuation level of the key process parameter is 0.

[0120] Furthermore, the deviation level of the critical process parameter at the nth critical process node can be determined based on the target quantity of the critical process parameter, the average value of the critical process parameter, and the deviation range of the critical process parameter. Including: According to the formula: Determine the deviation level of the key process parameters for the nth critical process node. ,in, Let n be the target quantity of the key process parameter for the nth critical process node. This represents the average value of the critical process parameters at the nth critical process node. The range of deviations for the critical process parameters at the nth critical process node. The difference between the average value of the critical process parameter at a critical process node and the target value of the critical process parameter represents the average deviation of the critical process parameter. This is true when the following conditions are met. When the condition is met, that is, when the average deviation of the key process parameter is less than or equal to the deviation range of the key process parameter, The ratio of the average deviation of a critical process parameter to the range of deviations for that critical process parameter indicates the degree of average deviation of the critical process parameter. The smaller the value, the smaller the average deviation of the key process parameter; the larger the deviation level of the key process parameter (the larger the deviation level of the key process parameter, the more stable it is); and the better the stability of the average deviation of the key process parameter. The larger the value, the greater the average deviation of the key process parameter; the smaller the deviation level of the key process parameter (the smaller the deviation level of the key process parameter, the more unstable it is), the worse the stability of the average deviation of the key process parameter. When the condition is met, that is, when the average deviation of the critical process parameter is greater than the target value of the critical process parameter, the average deviation of the critical process parameter exceeds the limit of product production, affecting the average deviation of the critical process parameter at the next critical node, and even affecting the product quality and process stability of the process step, the critical process parameter deviation level is 0; based on the arithmetic mean of the fluctuation level of the critical process parameter at each critical node, and the product of the critical process parameter deviation levels at each critical node, the controllability of the process parameters representing process stability is determined, including: according to the formula: Determine the controllability of process parameters that represent process stability. The arithmetic mean of the fluctuation level of the critical process parameter at each critical node and the product of the deviation level of the critical process parameter at each critical node are multiplied together. This process considers both the fluctuation degree of the critical process parameter at an individual critical node and the average deviation of the critical process parameters across all critical nodes, thus quantifying the overall stability of the process. The larger the value of each item, the greater the controllability of the process parameters. The larger the size, the stronger the process stability.

[0121] According to an embodiment of the present invention, in step S6, the optimal production process flow is determined based on the green production parameters of the process and the controllability of the process parameters.

[0122] Figure 5 A schematic diagram illustrating the determination of the optimal production process flow according to an embodiment of the present invention is shown as an example.

[0123] According to an embodiment of the present invention, step S6 includes:

[0124] Step S61: Determine the green production parameter standard and the controllability standard of the process parameter based on the green production parameters and the controllability of the process parameters.

[0125] Step S62: Calculate the green production parameters of each alternative process scheme under each process, compare them with the green production parameter standard, and thus determine multiple candidate production process flows that meet the green requirements.

[0126] Step S63: Calculate the controllability of process parameters for each candidate production process, compare it with the controllability standard of process parameters, and determine the final production process that performs best in terms of greenness and controllability as the optimal production process.

[0127] For example, green production parameter standards are used to evaluate and classify the degree of greening of alternative production process schemes for each production step. For instance, when... When the greenness level of the alternative production process is excellent, it indicates that the solution performs exceptionally well in terms of output quality and energy consumption, has high resource utilization, and is suitable as the preferred production process solution. When the greening level of the alternative production process is good, it indicates that the solution has reached the advanced level in the industry, the green production indicators are healthy, and it meets the requirements of sustainable development. Therefore, it can be considered as a secondary production process option. At that time, the greening level of the alternative production process is qualified, and the output quality and energy consumption level are within an acceptable range. However, one parameter may be slightly worse, leaving room for optimization. Generally, it is not considered as an alternative. When the greening level of the alternative production process is unqualified, deviating significantly from green production standards, and potentially involving high energy consumption, high waste, or large environmental fluctuations, it is the first alternative to be eliminated. The process parameter controllability standard is used to evaluate and classify the overall process stability. For example, when... When the process parameters are controllable, it indicates a high degree of process stability, with all key process parameters maintaining good stability and achieving their target values. This demonstrates strong process consistency and stable product quality. At this time, the controllability of process parameters is qualified, and the stability of most key process parameters remains good. However, some key process parameters deviate significantly from their target values, resulting in decreased process stability and potential fluctuations in product quality, such as batch differences. At this time, the controllability of process parameters is unqualified, the stability of most key process parameters exceeds the allowable range, and the deviation from the target values ​​of key process parameters is large. The production process is on the verge of being out of control, and the product quality risk is high, such as substandard performance and high scrap rate. The production process flow is arranged according to the alternative solutions that meet the constraints of production efficiency and cost, including: According to the formula: Determine the green production parameters for the entire production process. ,in, Green parameters for the i-th process step That is, the green production parameters of each alternative scheme under each process step. Used to measure the overall greenness of a production process, providing quantitative evidence for the selection and ranking of alternative production processes; calculating the controllability of process parameters for each production process, and selecting production processes that meet the stability requirements of process parameters, including: according to the formula: Lgs The system green synergy level Lgs, which reflects the degree of synergy between the greenness and controllability of the production process, is determined. Green parameters for the entire production process. To ensure the controllability of process parameters, and The larger the value, the greater the level of green collaboration in the system. The better the production process performs in terms of both greenness and controllability. The production process with the highest level of green collaboration in the system is the optimal production process.

[0128] According to an embodiment of the present invention, in step S7, a corresponding green production engineering construction plan for graphene-based electrode materials is formulated and implemented based on the optimal production process flow, and the production operation status is dynamically optimized.

[0129] Figure 6 An exemplary schematic diagram of dynamic optimization of production operation status according to an embodiment of the present invention is shown.

[0130] According to an embodiment of the present invention, step S7 includes:

[0131] Step S71: Determine the production operation status based on the green production parameters and the controllability of the process parameters, wherein the production operation status includes: green production balance status and green production imbalance status.

[0132] Step S72: Based on the optimal production process flow, predict the probability of a green imbalance state in production, and determine the construction plan for the green production engineering of graphene-based electrode materials based on the predicted imbalance state probability and the production operation status.

[0133] Step S73: When the condition that the production operation status is in a green balance state is met, a dynamic optimization instruction to maximize output is triggered.

[0134] Step S74: When the conditions for production operation to be in a state of green imbalance are met, a dynamic optimization instruction for slowing down and stabilizing is triggered.

[0135] For example, the production and operation status of graphene-based electrode materials can be determined based on the green parameters and controllability of the process parameters. For instance, when the following conditions are met... and When the conditions are met, the production status is judged to be highly green and the process stability is highly controllable, with stable product quality, and it is in the green balance zone of production, that is, the green balance state of production. and / or Under certain conditions, the current production status is judged to be relatively poor in terms of greening, with decreased process stability and fluctuating product quality, placing it in a green imbalance zone, i.e., a green imbalance state in production. The green imbalance state prediction model is a type of neural network model, including: a data preprocessing and input layer, a feature extraction layer, a feature fusion layer, and a prediction and output layer. The model is trained using historical data to output the probability of a green imbalance state occurring when production follows the currently set optimal production process; that is, it predicts the probability of an imbalance state. The input and output of the green imbalance state prediction model are the production parameters of the optimal production process (e.g., equipment power). Based on the predicted probability of imbalance and production operation status, a green production engineering construction plan for graphene-based electrode materials, including production equipment and waste treatment, is determined. When the production status is in a green balance state, the production process does not need to be adjusted and the capacity maximization mode can be executed to accelerate the production line operation rate, maximize product output, and continuously conduct dynamic monitoring-feedback loop. When the production status is in a green imbalance state, a speed reduction and stability maintenance command needs to be triggered. By reducing the production rate and optimizing the production process in a targeted manner, the green parameters of the process production and the controllability of the process parameters are improved so that the production status returns to the green balance zone. Only then can the production rate restriction be lifted and the dynamic monitoring-feedback loop continue.

[0136] According to an embodiment of the present invention, step S72 includes:

[0137] Step S721: Determine the probability of the actual imbalance state in the k-th production stage based on the green balance state and green imbalance state at multiple moments in the k-th production stage.

[0138] Step S722: When k equals 0, determine the green production engineering construction plan for graphene-based electrode materials in the (k+1)th production stage based on the predicted imbalance state probability of the (k+1)th production stage.

[0139] Step S723: When k is greater than or equal to 1, determine the comprehensive predicted imbalance probability based on the predicted imbalance probability of the (k+1)th production stage and the actual imbalance probability of the kth production stage, and determine the green production engineering construction plan for the graphene-based electrode material of the (k+1)th production stage based on the comprehensive predicted imbalance probability.

[0140] For example, the actual imbalance probability of the k-th production stage is determined based on the ratio of the number of green imbalance states to the number of green balance states at multiple moments in k production stages. When k=0, i.e., before all production stages have started, the green production engineering construction plan for graphene-based electrode materials in the first production stage is determined based on the predicted imbalance probability of the first production stage. For instance, when the predicted imbalance probability of the first production stage is greater than or equal to 0.3, the system automatically compares the alternative process flows and finds the alternative production process with a predicted imbalance probability less than 0.3 (when the predicted imbalance probability of all plans is greater than or equal to 0.3, the alternative production process with the lowest predicted imbalance probability is selected). The system then re-determines the required production equipment, resource consumption types, and waste discharge methods based on the alternative process flows. Based on the production equipment, the system determines the equipment selection and layout optimization plan, prioritizing energy-saving and low-pollution equipment. The system determines green logistics solutions for raw material supply and warehousing to reduce energy consumption and pollution during transportation and storage. It also determines environmentally friendly treatment and resource utilization solutions for waste gas, wastewater, and waste residue based on waste discharge methods to meet green production emission standards. When k is greater than or equal to 1, the system determines the comprehensive predicted imbalance probability based on the predicted imbalance probability of the (k+1)th production stage and the actual imbalance probability of the kth production stage. Specifically, it calculates the average of the predicted imbalance probability of the (k+1)th production stage and the actual imbalance probability of the kth production stage. When the comprehensive predicted imbalance probability is greater than 0.3, the system automatically compares alternative process flows and finds alternative production processes with a comprehensive predicted imbalance probability less than 0.3 (when the comprehensive predicted imbalance probability of all processes is greater than or equal to 0.3, the alternative production process with the lowest comprehensive predicted imbalance probability is selected). The system then executes corresponding operations on production equipment, resource consumption types, and waste discharge methods.

[0141] The green production engineering construction method for graphene-based electrode materials according to embodiments of the present invention can obtain the process production steps of graphene-based electrode materials, determine the production process flow based on the process production steps, and obtain product production data and production environment data of the product production workshop during the production process. Based on the product production data and production environment data, process production green parameters that measure the degree of greening of the production process are determined. At the same time, based on the production process flow and product production data, the controllability of process parameters that represent process stability is determined. Furthermore, based on the process production green parameters and the controllability of process parameters, the optimal production process flow is determined. Finally, based on the optimal production process flow, a corresponding green production engineering construction plan for graphene-based electrode materials is formulated and implemented, and the production operation status is dynamically optimized, thereby improving the comprehensiveness of the green production engineering construction method for graphene-based electrode materials. When determining the green parameters of the production process, the green parameters of the production process can be calculated using a composite function model based on the product qualification rate, unit time capacity, unit time product energy consumption, unit time product waste, actual environmental factor values, environmental factor thresholds, environmental factor compliance ranges, and environmental control system power. In the calculation process, the contribution of the green efficiency of output, environmental control energy consumption, and environmental factor compliance to the green parameters of the production process is comprehensively considered, which improves the accuracy of the green parameters of the production process.

[0142] Figure 7 An exemplary block diagram of a green production engineering construction system for graphene-based electrode materials according to an embodiment of the present invention is shown, the system comprising:

[0143] The process flow module is used to obtain the process production steps of graphene-based electrode materials and determine the production process flow based on the process production steps.

[0144] The production data module is used to continuously collect product production data during the production process of graphene-based electrode materials and integrate the collected product production data into the manufacturing execution system dashboard.

[0145] The environmental data module is used to deploy environmental sensors on the graphene-based electrode material production line to acquire production environment data of the product manufacturing workshop.

[0146] The green parameter module is used to determine process production green parameters that measure the degree of greening of the production process based on the product production data and the production environment data.

[0147] The controllability module is used to determine the controllability of process parameters representing process stability based on the production process flow and the product production data.

[0148] The optimal process module is used to determine the optimal production process flow based on the green production parameters of the process and the controllability of the process parameters.

[0149] The engineering construction module is used to formulate and implement corresponding green production engineering construction plans for graphene-based electrode materials based on the optimal production process flow, and to dynamically optimize the production operation status.

[0150] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0151] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A green production engineering construction method for graphene-based electrode materials, characterized in that, include: The process steps for obtaining graphene-based electrode materials are defined, and the production process flow is determined based on the process steps. During the production of graphene-based electrode materials, product production data is continuously collected and integrated into the manufacturing execution system dashboard. Environmental sensors are deployed on the graphene-based electrode material production line to acquire production environment data from the product manufacturing workshop; Based on the product production data and the production environment data, determine the green production parameters for measuring the degree of greening of the production process; Based on the production process flow and the product production data, determine the controllability of process parameters that represent process stability; The optimal production process flow is determined based on the green production parameters of the process and the controllability of the process parameters. Based on the optimal production process, a corresponding green production engineering construction plan for graphene-based electrode materials was formulated and implemented, and the production operation status was dynamically optimized.

2. The green production engineering construction method for graphene-based electrode materials according to claim 1, characterized in that, The process steps for obtaining graphene-based electrode materials, and the production process flow determined based on the stated process steps, include: The process steps for obtaining graphene-based electrode materials include: raw material pretreatment process, material modification and integration process, and resource recycling process. For the aforementioned production steps, multiple interchangeable alternative production process schemes are generated; A preliminary evaluation of the proposed production process options is conducted, and a set of options that meet the constraints of production efficiency and cost are selected and arranged into a production process flow.

3. The green production engineering construction method for graphene-based electrode materials according to claim 1, characterized in that, Based on the product production data and the production environment data, determine the green production parameters for measuring the degree of greening of the production process, including: Based on the product production data, obtain the product qualification rate, unit time production capacity, unit time product energy consumption, and unit time product waste. Based on the production environment data, obtain the actual environmental factor values ​​that characterize the workshop environment. Obtain environmental factor thresholds, acceptable ranges for environmental factors, and the power of environmental control systems; Based on the product qualification rate, the unit time production capacity, the unit time product energy consumption, the unit time product waste, the actual environmental factor value, the environmental factor threshold, the environmental factor qualification range, and the environmental control system power, the process production green parameters that measure the degree of greening of the production process are calculated and determined through a composite function model.

4. The green production engineering construction method for graphene-based electrode materials according to claim 3, characterized in that, Based on the product qualification rate, unit-time production capacity, unit-time product energy consumption, unit-time product waste volume, actual environmental factor values, environmental factor thresholds, environmental factor acceptable ranges, and environmental control system power, process production green parameters for measuring the degree of greening of the production process are calculated and determined through a composite function model, including: according to the formula: ， Determine the green production parameters for the i-th process step. ,in, Let be the product qualification rate of the i-th process step. Let be the unit time capacity of the i-th process step. Let i be the product energy consumption per unit time for the i-th process step. Let i be the amount of product waste per unit time in the i-th process step. For the preset unit time length, For the power of the environmental control system, Let j be the actual environmental factor value. Let the threshold be the j-th environmental factor. For the j-th environmental factor, the acceptable range is... Let J be the preset waste energy consumption coefficient, j≤J, where J is the number of environmental factors, and both j and J are positive integers.

5. The green production engineering construction method for graphene-based electrode materials according to claim 1, characterized in that, Based on the production process flow and the product production data, determine the controllability of process parameters representing process stability, including: Based on the aforementioned production process flow, identify and define the key process nodes that have a critical impact on the final product quality and process stability. Based on the key process nodes and the product production data, determine the maximum value of the change in key process parameters, the minimum value of the change in key process parameters, the allowable fluctuation range of the change in key process parameters, the target value of key process parameters, the average value of key process parameters, and the deviation range of key process parameters; The fluctuation level of the key process parameter is determined based on the maximum value of the change in the key process parameter, the minimum value of the change in the key process parameter, and the allowable fluctuation range of the change in the key process parameter. The deviation level of the key process parameters is determined based on the target quantity of the key process parameters, the average value of the key process parameters, and the deviation range of the key process parameters. Based on the fluctuation level and deviation level of the key process parameters, the controllability of process parameters representing process stability is determined.

6. The green production engineering construction method for graphene-based electrode materials according to claim 5, characterized in that, Based on the fluctuation level and deviation level of the key process parameters, the controllability of process parameters representing process stability is determined, including: When the condition that the fluctuation of the change in the key process parameter is less than or equal to the allowable fluctuation range of the change in the key process parameter is met, the fluctuation level of the key process parameter is 1 minus the relative difference between the fluctuation of the change in the key process parameter and the allowable fluctuation range of the change in the key process parameter. When the fluctuation of the change in the key process parameter is greater than the allowable fluctuation range of the change in the key process parameter, the fluctuation level of the key process parameter is 0. When the condition that the average deviation of the key process parameter is less than or equal to the deviation range of the key process parameter is met, the deviation level of the key process parameter is 1 minus the relative difference between the average deviation of the key process parameter and the deviation range of the key process parameter. When the average deviation of the key process parameter is greater than the deviation range of the key process parameter, the deviation level of the key process parameter is 0. The controllability of process parameters representing process stability is determined by the arithmetic mean of the fluctuation levels of key process parameters at each critical node and the product of the deviation levels of key process parameters at each critical node.

7. The green production engineering construction method for graphene-based electrode materials according to claim 1, characterized in that, Based on the green production parameters and the controllability of the process parameters, the optimal production process flow is determined, including: Based on the green production parameters of the process and the controllability of the process parameters, determine the standards for green production parameters and the standards for controllability of process parameters. Calculate the green production parameters of each alternative process scheme under each process, compare them with the green production parameter standards, and thus determine multiple candidate production process flows that meet the green requirements. Calculate the controllability of process parameters for each candidate production process, compare it with the controllability standard of process parameters, and determine the final production process that performs best in terms of greenness and controllability as the optimal production process.

8. The green production engineering construction method for graphene-based electrode materials according to claim 1, characterized in that, Based on the optimal production process, a corresponding green production engineering construction plan for graphene-based electrode materials was formulated and implemented, and the production operation status was dynamically optimized, including: Based on the green production parameters of the process and the controllability of the process parameters, the production operation status is determined, wherein the production operation status includes: green production balance status and green production imbalance status. Based on the optimal production process, the probability of a green imbalance state in production is predicted, and based on the predicted imbalance state probability and the production operation status, a green production engineering construction plan for graphene-based electrode materials is determined. When the conditions for production operation to be in a state of green balance are met, a dynamic optimization instruction to maximize output is triggered. When the conditions for production operation to be in a state of green imbalance are met, a dynamic optimization instruction to reduce speed and stabilize is triggered.

9. The green production engineering construction method for graphene-based electrode materials according to claim 8, characterized in that, Based on the optimal production process flow, the probability of a green imbalance state occurring in production is predicted. Based on the predicted imbalance state probability and the production operation status, a green production engineering construction plan for graphene-based electrode materials is determined, including: Based on the green balance and imbalance states of production at multiple moments in the k-th production stage, determine the probability of the actual imbalance state in the k-th production stage. When k equals 0, the construction plan for the green production of graphene-based electrode materials in the (k+1)th production stage is determined based on the predicted imbalance probability of the (k+1)th production stage. When k is greater than or equal to 1, the comprehensive predicted imbalance probability is determined based on the predicted imbalance probability of the (k+1)th production stage and the actual imbalance probability of the kth production stage, and the construction plan for the green production project of graphene-based electrode materials in the (k+1)th production stage is determined based on the comprehensive predicted imbalance probability.

10. A green production engineering construction system for graphene-based electrode materials, characterized in that, For performing the method according to any one of claims 1-9, comprising: The process flow module is used to obtain the process production steps of graphene-based electrode materials and determine the production process flow based on the process production steps. The production data module is used to continuously collect product production data during the production process of graphene-based electrode materials and integrate the collected product production data into the manufacturing execution system dashboard. The environmental data module is used to deploy environmental sensors on the graphene-based electrode material production line to acquire production environment data of the product manufacturing workshop. The green parameter module is used to determine process production green parameters that measure the degree of greening of the production process based on the product production data and the production environment data. The controllability module is used to determine the controllability of process parameters representing process stability based on the production process flow and the product production data. The optimal process module is used to determine the optimal production process flow based on the green production parameters of the process and the controllability of the process parameters. The engineering construction module is used to formulate and implement corresponding green production engineering construction plans for graphene-based electrode materials based on the optimal production process flow, and to dynamically optimize the production operation status.

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