Cable reel carbon footprint prediction control method, device and equipment and storage medium

Abstract

The invention discloses a cable reel carbon footprint prediction control method, device and equipment and a storage medium, and the method comprises the steps: constructing a carbon footprint prediction control model according to the carbon emission standardized data of different stages of the whole life cycle of a target cable reel, and performing predictive control training on the carbon footprint predictive control model based on a deep learning network, generating a carbon footprint predictive control scheme according to the carbon footprint predictive control model, and performing predictive control on the carbon footprint of the target cable reel in the life cycle by using the carbon footprint predictive control scheme. According to the invention, through detailed life cycle stage division, standardized processing of carbon emission and prediction control training based on the deep learning network, the carbon emission characteristics of the cable reel in different life stages and the non-quantitative response relationship between the carbon emission characteristics are accurately captured, and the accuracy of carbon footprint prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission control, and in particular, to a method, device, equipment and storage medium for predicting and controlling the carbon footprint of a cable reel. Background Art

[0002] With the increasingly severe global climate change problem, the monitoring and management of carbon emissions have become an important issue that needs to be solved urgently in various industries. As a common device in the industrial and construction fields, the carbon footprint of a cable reel covers multiple stages such as production, transportation, use, and maintenance. Accurately evaluating the carbon footprint of a cable reel is of great significance for formulating effective emission reduction strategies and improving energy utilization efficiency.

[0003] Traditional carbon footprint calculation methods usually use simple mathematical models or statistical methods for carbon footprint prediction, and rely on limited historical emission data or data from a single source for prediction. They fail to make full use of multi-source and multi-dimensional data resources, thus limiting the improvement of prediction accuracy. Moreover, the prediction and control methods lack the ability to be flexibly adjusted and lack an effective optimization mechanism.

[0004] Therefore, how to predict and control the carbon footprint of the entire life cycle of a cable reel has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, equipment and storage medium for predicting and controlling the carbon footprint of a cable reel to solve the problem of carbon footprint control of the cable reel.

[0006] To solve the above technical problem, an embodiment of the present invention provides a method for predicting and controlling the carbon footprint of a cable reel, including: Dividing the carbon footprint of the target cable reel into stages according to the life cycle information of the target cable reel.

[0007] Collecting and masking the carbon emissions of each stage of the target cable reel to obtain normalized carbon emission data.

[0008] Constructing a carbon footprint prediction and control model based on the normalized carbon emission data of each stage, and performing carbon emission prediction and control training on the carbon footprint prediction and control model based on a deep learning network. The carbon emission prediction and control training aims to minimize the total carbon footprint, and generates carbon emission adjustment factors for each stage according to the non-quantitative response relationship between the carbon emissions of each stage.

[0009] Generating a carbon footprint prediction and control plan according to each carbon emission adjustment factor, and predicting and controlling the carbon footprint of the target cable reel in its life cycle with the carbon footprint prediction and control plan.

[0010] Further, the step of dividing the carbon footprint of the target cable reel according to the life cycle information of the target cable reel includes: According to the life cycle information of the target cable reel, the carbon footprint of the target cable reel is divided into a raw material acquisition stage, a manufacturing stage, a transportation stage, a usage stage, and a recycling and treatment stage.

[0011] Further, the step of collecting the carbon emissions of each stage of the target cable reel and performing masking processing to obtain normalized carbon emission data includes: Collect the carbon emissions of each stage of the target cable reel to obtain segmented carbon emission data for each stage.

[0012] Numerically process the non-numerical variables in each of the segmented carbon emission data, and uniformly encode each of the segmented carbon emission data to obtain normalized carbon emission data.

[0013] Further, the step of collecting the carbon emissions of each stage of the target cable reel to obtain segmented carbon emission data for each stage includes: Obtain the carbon emission collection data of the target cable reel in each stage, perform data point comparison and analysis on each of the carbon emission collection data and the standardized carbon emission data, and determine whether there is data missing in the carbon emission collection data. If so, use a generative adversarial network to complete the missing values to obtain segmented carbon emission data for each stage.

[0014] Further, the step of constructing a carbon footprint prediction and control model according to the normalized carbon emission data of each stage and performing carbon emission prediction and control training on the carbon footprint prediction and control model based on a deep learning network includes: Construct a carbon footprint prediction and control model according to the normalized carbon emission data of each stage.

[0015] Perform carbon emission prediction and control training on the carbon footprint prediction and control model based on a deep learning network to obtain carbon emission prediction data for each stage.

[0016] Use the Monte Carlo method to simulate the carbon emission situations in different stages, perform correlation analysis on the carbon emission prediction data of each stage, and obtain a non-quantitative response relationship between the carbon emissions of each stage.

[0017] Further, the step of generating a carbon footprint prediction and control plan according to each of the carbon emission adjustment factors and performing prediction and control on the carbon footprint of the target cable reel in its life cycle with the carbon footprint prediction and control plan includes: Calculate the adjustment values of the energy consumption of each stage of the target cable reel according to each of the carbon emission adjustment factors to generate a carbon footprint prediction and control plan.

[0018] Predict and control the carbon footprint of the target cable reel during its life cycle according to the adjustment value in the carbon footprint prediction control scheme.

[0019] Furthermore, the method further includes: During the process of predicting and controlling the carbon footprint of the target cable reel according to the carbon footprint prediction control scheme, record the actual carbon emission data of the target cable reel.

[0020] Optimize and adjust the carbon footprint prediction control scheme according to the actual carbon emission data.

[0021] Another embodiment of the present invention provides a cable reel carbon footprint prediction control device, including: A stage division module, configured to divide the carbon footprint of the target cable reel according to the life cycle information of the target cable reel.

[0022] A data acquisition module, configured to collect the carbon emissions of each stage of the target cable reel and perform masking processing to obtain normalized carbon emission data.

[0023] A model training module, configured to construct a carbon footprint prediction control model according to the normalized carbon emission data of each stage, and perform carbon emission prediction control training on the carbon footprint prediction control model based on a deep learning network. The carbon emission prediction control training aims to minimize the total carbon footprint and generate carbon emission adjustment factors for each stage according to the non-quantitative response relationship between the carbon emissions of each stage.

[0024] A carbon footprint control module, configured to generate a carbon footprint prediction control scheme according to each carbon emission adjustment factor, and predict and control the carbon footprint of the target cable reel during its life cycle with the carbon footprint prediction control scheme.

[0025] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the cable reel carbon footprint prediction control method described above is implemented.

[0026] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the cable reel carbon footprint prediction control method described above is implemented.

[0027] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: Through meticulous lifecycle stage division, normalization of carbon emissions, and predictive control training based on deep learning networks, the carbon emission characteristics of cable reels at different life stages and their non-quantitative response relationships are accurately captured, improving the accuracy of carbon footprint prediction. It also enables managers to quickly adjust carbon emission strategies based on real-time data, enhancing the refinement level and response speed of carbon footprint management.

[0028] Guided by the generated carbon emission adjustment factors and carbon footprint prediction control schemes, the optimal allocation of energy during the production and use of cable reels is optimized, reducing unnecessary energy consumption and carbon emissions. This helps enterprises reduce production costs, improve economic benefits, and reduce greenhouse gas emissions, thus contributing to the global trend of green, low-carbon, and sustainable development. Brief Description of the Drawings

[0029] Figure 1 It is a flowchart of the steps of the cable reel carbon footprint prediction control method provided by the embodiment of the present invention; Figure 2 It is a structural block diagram of the cable reel carbon footprint prediction control device provided by the embodiment of the present invention; Figure 3 It is a structural diagram of the computer device provided by the embodiment of the present invention. Detailed Embodiments

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0031] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0032] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "linkage" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0033] In the description of the present application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0034] An embodiment of the present invention provides a method for predicting and controlling the carbon footprint of a cable reel. Specifically, please refer to Figure 1 , the method for predicting and controlling the carbon footprint of a cable reel in one embodiment of the present invention includes steps S11 to S14: Step S11: Divide the carbon footprint of the target cable reel according to the life cycle information of the target cable reel.

[0035] The carbon footprint of the cable reel covers the entire life cycle from raw material acquisition to recycling. The carbon footprint is the sum of carbon emissions at each stage of the life cycle of the target cable reel. The carbon footprint of the cable reel is not limited to a single link, but runs through its entire life cycle. Only by comprehensively considering the emissions at each stage can the impact on the environment be accurately evaluated. In order to reduce the overall carbon footprint, corresponding measures and technical means need to be taken at each stage. Therefore, it is necessary to divide the carbon footprint stages of the cable reel.

[0036] According to the life cycle information of the target cable reel, the carbon footprint of the target cable reel is divided into a raw material acquisition stage, a production and manufacturing stage, a transportation stage, a use stage, and a recycling stage.

[0037] By subdividing the stages, the main sources and key links of carbon emissions can be identified, so as to formulate targeted emission reduction measures.

[0038] Step S12: Collect the carbon emissions of each stage of the target cable reel and perform masking processing to obtain normalized carbon emission data.

[0039] Collect the carbon emission data of each of the above stages. Specifically, the collection process for each stage is as follows: Raw material acquisition stage: Collect information such as the types, sources, and transportation distances of the cable reel raw materials, as well as the energy consumption and carbon emissions during the production of the raw materials.

[0040] Production and manufacturing stage: Record data such as energy consumption, raw material usage, and waste generation during the production of the cable reel.

[0041] Transportation stage: Install a GPS positioning module on the cable reel, accurately obtain the transportation trajectory of the cable reel through GPS (including detailed information such as transportation distance and route), and record the transportation tool information.

[0042] Usage stage: Consider the energy consumption during the processes of wear, maintenance, wire loading, and wire unreeling of the cable reel during use.

[0043] Recycling and disposal stage: Obtain the recycling and disposal methods, recovery rates, and energy consumption and carbon emissions during the processing of cable reels made of different materials.

[0044] After completing the data collection, it is necessary to calculate the carbon emissions of each stage. The specific process is as follows: Carbon emissions in the raw material acquisition stage: Calculate the carbon emissions of this stage based on the types and quantities of the raw materials, combined with the carbon emission factors during the production of the raw materials. Let the types of raw materials be , the weight of the i-th raw material be , its carbon emission factor during the production process be , the transportation distance of the i-th raw material be , the carbon emission per ton per kilometer during transportation be , then the carbon emissions in the raw material acquisition stage .

[0045] Carbon emissions in the production and manufacturing stage: Calculate the carbon emissions in the production and manufacturing stage through the energy consumption data during the production process and the corresponding carbon emission factors. At the same time, consider the carbon emissions generated from waste treatment. Let the types of energy consumed during the production process be , the -th type of energy consumption be , its carbon emission factor be , the weight of the waste generated during the production process be , the carbon emission factor for waste treatment be , then the carbon emissions in the production and manufacturing stage .

[0046] Carbon emissions during the transportation stage: Calculate the carbon emissions during the transportation stage based on the transportation distance, energy consumption of the transportation vehicle, and carbon emission factor. Let the transportation distance be , the carbon emission factor of the energy consumption of the transportation vehicle be , the weight of the cable reel be , .

[0047] Carbon emissions during the usage stage: Consider the carbon emissions generated from the energy consumption and maintenance activities during the use of the cable reel. Let the energy consumed for each maintenance be the maintenance energy , its carbon emission factor be , the number of maintenance times be Z, then the carbon emissions from maintaining the cable reel . Let the energy consumed during the cable wiring process be its carbon emission factor be G ， the cumulative wiring length be X meters, and the carbon emissions during the wiring stage be . Let the energy consumed during the cable unreeling process be , its carbon emission factor be H ， the cumulative unreeling length be Y meters, and the carbon emissions during the unreeling stage be . The carbon emissions during the usage stage are: + .

[0048] Carbon emissions during the recycling and disposal stage: Calculate the carbon emissions during the recycling and disposal stage based on the recycling and disposal methods and the recycling rate. Let the weight of the recyclable part be , the carbon emission factor during the recycling and disposal process be , the weight of the non-recyclable part be , the carbon emission factor for proper disposal be , then the carbon emissions during the recycling and disposal stage .

[0049] Sum up the carbon emissions of each stage to obtain the total carbon footprint of the cable reel: Among them, Collect production and usage data of a large number of cable reel samples, including various data required in the above carbon footprint calculation process and the carbon emissions at each stage obtained according to the above calculation method. According to the general cable reel life cycle information, calculate the carbon emissions in the two stages of raw material acquisition and production respectively. For the transportation and usage stages, calculate the carbon emissions for each month according to the number of months of the maximum service life (for example, if the maximum service life is 5 years, the usage stage is 60 months, and calculate according to the number of maintenance times, transportation distance and mode, wiring / rewiring length, etc. for each month). For the recycling and treatment, calculate the carbon emissions according to the recovery rate and recovery method. These data should cover different models of cable reels, different production and usage environments, etc. to ensure the diversity and representativeness of the data.

[0050] There are difficulties in collecting some data in the cable reel life cycle. Therefore, there may be some missing data in the collected data, so it is necessary to complete the missing values.

[0051] Obtain the carbon emission collection data of the target cable reel at each stage, conduct a data point comparison and analysis between the carbon emission collection data and the standardized carbon emission data. By comparing the number of expected data points and the actually collected data points, judge whether there is missing data in the carbon emission collection data. If so, use a generative adversarial network to complete the missing values to obtain the segmented carbon emission data at each stage.

[0052] In order to predict the carbon footprint of cable reels at any stage in future stages, it is necessary to standardize the data at different stages. First, numericalize the non-numerical variables in the existing dataset. For example, encode the types of raw materials, sources, transportation tools, recycling methods, etc. (the encoding method is not unique, as long as it can be processed by a deep neural network. For those without a size relationship, one-hot encoding can be used, and for those with a size relationship, 1-n digital encoding can be used). The data inputs are successively the numericalized information of the raw materials and the production stage, the transportation methods, transportation distances, maintenance times, wire loading lengths, and wire unreeling lengths for each month of the maximum usage month, the recovery rate and recovery method information in the recovery stage, and the corresponding labels (network outputs) are the carbon emissions from the initial stage to the two stages of raw materials and production, the carbon emissions from the initial stage to each month, and the carbon emissions from the initial stage to the recovery stage. In order to create data containing cable reel information and carbon emission data at each stage through the existing collected data, multiply the input data mask and the corresponding label mask with the input data and labels respectively. Among them, both the input data mask and the label mask are vectors composed of 0 and 1, and their lengths are the same as the lengths of the input variables and output variables respectively. In order to simulate different production stages, create different stage masks. For example, when simulating the raw material acquisition stage of the cable reel, that is, the raw material acquisition stage in the input data mask is 1, and the other bits are 0, and the raw material carbon emissions in the output mask are 1, and the other bits are 0; when simulating that the cable reel has been used for 12 months (the maximum period is 60 months), the values from the first bit to the 12th month of use in the input data mask are 1, and the remaining bits are 0, and the emissions from the first bit to the 12th month in the output mask are 1, and the remaining bits are 0. Multiply the data with the respective stage masks respectively, and the standardized cable reel data and carbon emission standardized data at each stage can be obtained.

[0053] Mask processing can ensure the standardization and universality of carbon emission data while maintaining the accuracy of the data. Through standardization processing, the data becomes easier to manage and analyze.

[0054] Step S13: Construct a carbon footprint prediction control model based on the carbon emission standardized data at each stage, and perform carbon emission prediction control training on the carbon footprint prediction control model based on a deep learning network. The carbon emission prediction control training aims to minimize the total carbon footprint and generate carbon emission adjustment factors for each stage according to the non-quantitative response relationship between the carbon emissions at each stage.

[0055] Since there is an interactive but non - quantitative relationship among the carbon emissions of each stage of the cable reel, an increase or decrease in carbon emissions at a certain stage will have more or less impact on the carbon emissions of other stages. Therefore, it is necessary to analyze the non - quantitative response relationship among the carbon emissions of each stage. Specifically, in this embodiment, the Monte Carlo method is used to simulate the carbon emissions of different stages, and a correlation analysis is performed on the predicted carbon emission data of each stage to obtain the non - quantitative response relationship among the carbon emissions of each stage.

[0056] Construct a carbon footprint prediction and control model based on the normalized data of carbon emissions of each stage.

[0057] Based on the deep - learning network, conduct carbon emission prediction and control training on the carbon footprint prediction and control model. During the training process, predict the carbon emission data of each stage.

[0058] According to the sum of the predicted carbon emission data of each stage, obtain the predicted carbon footprint. With the goal of minimizing the total carbon footprint, dynamically adjust the carbon emission quota indicators of each stage according to the non - quantitative response relationship, so as to generate the carbon emission adjustment factors of each stage corresponding to the optimal carbon footprint.

[0059] Using the Monte Carlo method to analyze the non - quantitative response relationship among the carbon emissions of each stage of the cable reel, and accordingly constructing a carbon footprint prediction and control model for carbon emission prediction and control training is of great significance for improving the scientificity and standardization of carbon emission management.

[0060] Step S14: Generate a carbon footprint prediction and control plan according to each carbon emission adjustment factor, and use the carbon footprint prediction and control plan to predict and control the carbon footprint of the target cable reel during its life cycle.

[0061] Calculate the adjustment values of the energy consumption of each stage of the target cable reel according to each carbon emission adjustment factor, and generate a carbon footprint prediction and control plan.

[0062] Predict and control the carbon footprint of the target cable reel during its life cycle according to the adjustment values in the carbon footprint prediction and control plan.

[0063] Based on the calculation of the carbon emission adjustment factors, the carbon footprint of the cable reel during its life cycle can be predicted more accurately, and the energy consumption can be adjusted according to the prediction results, so as to achieve effective control of carbon emissions. By adjusting the energy consumption of each stage, the resource allocation can be optimized to ensure that while meeting the performance requirements of the cable reel, the carbon emissions are minimized to the greatest extent.

[0064] Preferably, during the process of predicting and controlling the carbon footprint of the target cable reel using the carbon footprint prediction and control plan, the cable reels can be sampled. For the sampled cable reels, record the actual carbon emission data of their entire life cycle to obtain their carbon footprints.

[0065] Based on the comparison and analysis of the measured data and the predicted carbon footprint, the reliability of the carbon footprint prediction control scheme is evaluated, and the carbon footprint prediction control scheme is optimized and adjusted.

[0066] The cable reel carbon footprint prediction control method of the present invention accurately captures the carbon emission characteristics of the cable reel at different life stages and their non - quantitative response relationships through detailed life - cycle stage division, standardized processing of carbon emissions, and prediction control training based on a deep - learning network. It improves the accuracy of carbon footprint prediction and enables managers to quickly adjust carbon emission strategies according to real - time data, enhancing the refinement level and response speed of carbon footprint management. By generating carbon emission adjustment factors and carbon footprint prediction control schemes, it guides the optimal allocation of energy in the production and use processes of cable reels, reduces unnecessary energy consumption and carbon emissions, helps enterprises reduce production costs, improve economic benefits, and reduce greenhouse gas emissions, thus contributing to the global trend of green, low - carbon, and sustainable development.

[0067] The embodiment of the present invention also provides a cable reel carbon footprint prediction control device for executing the cable reel carbon footprint prediction control method as described above. Figure 2 It is a structural block diagram of the cable reel carbon footprint prediction control device according to the embodiment of the present invention. The device includes: A stage division module 21 for dividing the carbon footprint of the target cable reel according to the life - cycle information of the target cable reel.

[0068] A data acquisition module 22 for collecting the carbon emissions of each stage of the target cable reel and performing masking processing to obtain standardized carbon emission data.

[0069] A model training module 23 for constructing a carbon footprint prediction control model based on the standardized carbon emission data of each stage, and performing carbon emission prediction control training on the carbon footprint prediction control model based on a deep - learning network. The carbon emission prediction control training aims to minimize the total carbon footprint and generates carbon emission adjustment factors for each stage according to the non - quantitative response relationship between the carbon emissions of each stage.

[0070] A carbon footprint control module 24 for generating a carbon footprint prediction control scheme according to each carbon emission adjustment factor, and predicting and controlling the carbon footprint of the target cable reel in its life cycle with the carbon footprint prediction control scheme.

[0071] The technical features and technical effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be elaborated herein. Each module in the above device can be implemented in whole or in part by software, hardware, and their combinations. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0072] The embodiments of the present invention also provide a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the cable reel carbon footprint prediction control method as described above.

[0073] The embodiments of the present invention also provide a computer device, Figure 3 which is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the cable reel carbon footprint prediction control method as described above.

[0074] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2,...). The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0075] The processor can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the computer device, and connects various parts of the computer device through various interfaces and lines.

[0076] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory can also be other volatile solid-state storage devices.

[0077] It should be noted that the above computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural block diagram is only an example of the computer device, and does not constitute a limitation on the computer device. It may include more or fewer components than those shown in the figure, or combine some components, or different components.

[0078] In summary, compared with the prior art, the beneficial effects of the cable reel carbon footprint prediction control method, device, equipment and storage medium provided by the embodiments of the present invention are at least one of the following: Through detailed life cycle stage division, normalization processing of carbon emissions, and prediction control training based on a deep learning network, the carbon emission characteristics of the cable reel at different life stages and their non-quantitative response relationships are accurately captured, improving the accuracy of carbon footprint prediction. It also enables managers to quickly adjust carbon emission strategies according to real-time data, enhancing the refinement level and response speed of carbon footprint management.

[0079] By using the generated carbon emission adjustment factor and carbon footprint prediction control scheme to guide the optimal allocation of energy in the production and use processes of the cable reel, unnecessary energy consumption and carbon emissions are reduced, which helps enterprises reduce production costs, improve economic benefits, and reduce greenhouse gas emissions, thus contributing to the global green, low-carbon, and sustainable development trend.

[0080] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A cable drum carbon footprint prediction and control method, characterized in that: include: Dividing the carbon footprint of the target cable drum into stages according to the life cycle information of the target cable drum; The carbon emissions of the target cable drum at each stage are collected and masked to obtain normalized data of carbon emissions; A carbon footprint prediction and control model is constructed according to the normalized data of the carbon emissions at each stage, and carbon emission prediction and control training is performed on the carbon footprint prediction and control model based on a deep learning network, wherein the carbon emission prediction and control training aims to minimize the total carbon footprint, and generates a carbon emission adjustment factor for each stage according to a non-quantitative response relationship between the carbon emissions at each stage; A carbon footprint prediction control scheme is generated according to each of the carbon emission adjustment factors, and the carbon footprint of the target cable reel during its life cycle is predicted and controlled using the carbon footprint prediction control scheme.

2. The cable drum carbon footprint prediction and control method according to claim 1, characterized in that: The step of dividing the carbon footprint of the target cable reel into stages according to the life cycle information of the target cable reel comprises: According to the life cycle information of the target cable reel, the carbon footprint of the target cable reel is divided into the raw material acquisition stage, the manufacturing stage, the transportation stage, the use stage and the recycling stage.

3. The cable drum carbon footprint prediction and control method according to claim 1, characterized in that: The carbon emissions at each stage of the target cable drum are collected and masked to obtain normalized carbon emissions data, including: The carbon emissions of the target cable drum at each stage are collected to obtain segmented carbon emissions data at each stage; The non-numerical variables in each of the segmented carbon emission data are numerically processed, and each of the segmented carbon emission data is uniformly coded to obtain the normalized carbon emission data.

4. The cable drum carbon footprint prediction and control method according to claim 3, characterized in that: The carbon emissions of the target cable drum at each stage are collected to obtain segmented carbon emissions data of each stage, including: The carbon emission collection data of the target cable reel at each stage is obtained, and a data point comparison analysis is performed on each of the carbon emission collection data and the standardized carbon emission data to determine whether the carbon emission collection data has data missing. If so, a generative adversarial network is used to complete the missing values ​​to obtain the segmented carbon emission data for each stage.

5. The cable drum carbon footprint prediction and control method according to claim 1, characterized in that: The step of constructing a carbon footprint prediction control model according to the normalized data of carbon emissions at each stage, and performing carbon emission prediction control training on the carbon footprint prediction control model based on a deep learning network includes: Constructing a carbon footprint prediction and control model based on the normalized data of carbon emissions at each stage; Based on the deep learning network, the carbon footprint prediction control model is trained to obtain carbon emission prediction data for each stage; The Monte Carlo method is used to simulate the carbon emissions in different stages, and the correlation analysis is performed on the carbon emission prediction data of each stage to obtain the non-quantitative response relationship between the carbon emissions in each stage.

6. The cable drum carbon footprint prediction and control method according to claim 1, characterized in that: The step of generating a carbon footprint prediction control scheme according to each of the carbon emission adjustment factors, and predicting and controlling the carbon footprint of the target cable drum during its life cycle by using the carbon footprint prediction control scheme, comprises: Calculating the adjustment value of the energy consumption of each stage of the target cable drum according to each of the carbon emission adjustment factors to generate a carbon footprint prediction control scheme; The carbon footprint of the target cable drum during its life cycle is predicted and controlled according to the adjustment value in the carbon footprint prediction control scheme.

7. The cable drum carbon footprint prediction and control method according to any one of claims 1 to 6, characterized in that: The method further comprises: In the process of predicting and controlling the carbon footprint of the target cable drum during its life cycle using the carbon footprint prediction and control scheme, recording actual carbon emission data of the target cable drum; The carbon footprint prediction control scheme is optimized and adjusted according to the actual carbon emission data.

8. A cable drum carbon footprint prediction control device, characterized in that: include: A stage division module, used for dividing the carbon footprint of the target cable drum into stages according to the life cycle information of the target cable drum; A data collection module is used to collect and mask the carbon emissions of the target cable drum at each stage to obtain normalized data of carbon emissions; A model training module, used to construct a carbon footprint prediction and control model according to the normalized data of the carbon emissions at each stage, and to perform carbon emission prediction and control training on the carbon footprint prediction and control model based on a deep learning network, wherein the carbon emission prediction and control training aims to minimize the total carbon footprint, and generates a carbon emission adjustment factor for each stage according to a non-quantitative response relationship between the carbon emissions at each stage; The carbon footprint control module is used to generate a carbon footprint prediction control scheme according to each of the carbon emission adjustment factors, and predict and control the carbon footprint of the target cable reel during its life cycle by using the carbon footprint prediction control scheme.

9. A computer device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the cable drum carbon footprint prediction and control method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the cable drum carbon footprint prediction and control method according to any one of claims 1 to 7 is implemented.

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