Carbon emission determination method and device, storage medium and program product

Abstract

The invention discloses a carbon emission determining method and device, a storage medium and a program product, and relates to the technical field of carbon emission, the carbon emission determining method comprises the steps that sensor data collected in real time in the welding process is obtained, the sensor data comprises energy consumption, and the energy consumption is stored in a storage medium; the type of the discharged greenhouse gas and the discharge amount of the greenhouse gas are determined; determining direct carbon emission in the welding process according to the greenhouse gas type and the greenhouse gas emission; according to the energy consumption, the welding material and carbon emission factors, indirect carbon emission in the welding process is determined, and the carbon emission factors comprise the carbon emission per unit energy and the carbon emission per unit welding material; and the total carbon emission in the welding process is determined according to the direct carbon emission and the indirect carbon emission, and the accuracy of carbon emission determination in the welding process is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental detection technology, and in particular to a method, device, storage medium and program product for determining carbon emissions. Background Art

[0002] As global awareness of environmental protection increases, carbon emissions in industrial production processes are receiving increasing attention. As an indispensable part of the manufacturing industry, the impact of energy consumption and carbon emissions on the environment in welding cannot be ignored.

[0003] At present, the calculation of carbon emissions in the welding process mainly relies on traditional manual records and intermittent data collection methods, which cannot provide continuous and comprehensive data support and lack intelligent data analysis methods. It is usually based on empirical estimates or simple energy consumption monitoring and lacks accuracy. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, storage medium and program product for determining carbon emissions, aiming to solve the technical problem of low accuracy in determining carbon emissions.

[0005] To achieve the above objectives, the present application proposes a method for determining carbon emissions, the method comprising:

[0006] Acquire sensor data collected in real time during the welding process, wherein the sensor data includes energy consumption, types of greenhouse gases emitted, and greenhouse gas emissions;

[0007] determining direct carbon emissions from the welding process based on the greenhouse gas types and the greenhouse gas emissions;

[0008] Determine the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, wherein the carbon emission factors include carbon emissions per unit of energy and carbon emissions per unit of welding materials;

[0009] The total carbon emissions during the welding process are determined according to the direct carbon emissions and the indirect carbon emissions.

[0010] In one embodiment, before the step of determining the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, the step further includes:

[0011] Obtaining the energy type corresponding to the energy consumption;

[0012] The carbon emission factor is determined based on the energy type.

[0013] In one embodiment, before the step of determining the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, the step further includes:

[0014] Obtaining the material type of the welding material;

[0015] The carbon emission factor is determined based on the material type.

[0016] In one embodiment, the method further comprises:

[0017] Extracting characteristic data related to carbon emissions from the sensor data, wherein the characteristic data affects the magnitude of carbon emissions;

[0018] A carbon emission report for the welding process is generated according to the characteristic data and the carbon emission amount.

[0019] In one embodiment, after the step of generating a carbon emission report in the welding process according to the characteristic data and the carbon emission amount, the step further includes:

[0020] Determine, according to the carbon emission report, that the carbon emission amount is greater than the target characteristic data corresponding to the preset threshold;

[0021] Determining welding parameters to be adjusted and / or process flow to be improved according to the target characteristic data;

[0022] An adjustment strategy is generated according to the welding parameters to be adjusted and / or the process to be improved.

[0023] In one embodiment, after the step of extracting characteristic data related to carbon emissions from the sensor data, the following steps are included:

[0024] According to the characteristic data and the carbon emissions, a preset neural network model is trained to obtain a prediction model, and the prediction model is used to predict the carbon emissions during the welding process.

[0025] In one embodiment, the step of extracting characteristic data related to carbon emissions from the sensor data further includes:

[0026] Performing time series analysis on the sensor data to generate time series data;

[0027] Determining a correlation between the time series data and the carbon emissions;

[0028] The characteristic data is determined according to the correlation.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a carbon emissions determination device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the carbon emissions determination method as described above.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for determining carbon emissions as described above are implemented.

[0031] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for determining carbon emissions as described above.

[0032] The present application provides a method for determining carbon emissions, by acquiring sensor data collected in real time during the welding process, the sensor data including energy consumption, greenhouse gas types emitted and greenhouse gas emissions; determining direct carbon emissions during the welding process based on greenhouse gas types and greenhouse gas emissions; determining indirect carbon emissions during the welding process based on energy consumption, welding materials and carbon emission factors, the carbon emission factors including carbon emissions per unit energy and carbon emissions per unit welding materials; determining total carbon emissions during the welding process based on direct carbon emissions and indirect carbon emissions. The present application uses sensors to continuously and comprehensively collect data during the welding process, calculates carbon emissions during the welding process based on the collected data and carbon emission factors, and improves the accuracy of determining carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0035] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the method for determining carbon emissions of the present application;

[0036] Figure 2 A schematic diagram of a process for determining carbon emissions according to Embodiment 2 of the present application;

[0037] Figure 3 A flow chart of the third embodiment of the method for determining carbon emissions of the present application;

[0038] Figure 4 A flow chart of the fourth embodiment of the method for determining carbon emissions of the present application;

[0039] Figure 5 A flow chart of the fifth embodiment of the method for determining carbon emissions of the present application;

[0040] Figure 6 A flow chart of the seventh embodiment of the method for determining carbon emissions of the present application;

[0041] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for determining carbon emissions in an embodiment of the present application.

[0042] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0044] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0045] As global awareness of environmental protection increases, carbon emissions in industrial production processes are receiving increasing attention. Welding technology is not only widely used in heavy industries such as ships, automobiles, and construction, but also plays an important role in high-tech industries such as precision instruments and electronic products. However, the welding process is often accompanied by high carbon emissions, which is closely related to factors such as the type of energy used, equipment efficiency, and welding materials.

[0046] At present, the calculation of carbon emissions in the welding process mainly relies on traditional manual records and intermittent data collection methods, which cannot provide continuous and comprehensive data support. These methods often rely on the experience and consciousness of the operators, and are prone to omissions or misrecording. In addition, the lack of intelligent data analysis methods makes it difficult to achieve refined monitoring and management of carbon emissions in the welding process. Usually, the estimation of carbon emissions is based on empirical formulas or simple energy consumption monitoring. This method is difficult to accurately reflect the actual carbon emissions, and it cannot provide a scientific basis for reducing carbon emissions.

[0047] In view of the above problems, the present application proposes a method for determining carbon emissions, which obtains sensor data collected in real time during the welding process, the sensor data including energy consumption, type of greenhouse gas emitted and greenhouse gas emissions; determines the direct carbon emissions in the welding process according to the greenhouse gas type and greenhouse gas emissions; determines the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, the carbon emission factors including carbon emissions per unit energy and carbon emissions per unit welding materials; determines the total carbon emissions in the welding process according to the direct carbon emissions and the indirect carbon emissions, thereby improving the accuracy of carbon emission determination.

[0048] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication and program running functions. In order to more clearly explain this technical solution, this application uses a carbon emission calculation server as the execution entity to illustrate this embodiment and the following embodiments. The server is used to calculate carbon emissions during the welding process.

[0049] Based on this, the first embodiment proposed in this application provides a method for determining carbon emissions, referring to Figure 1 In this embodiment, the method includes steps S10 to S40:

[0050] Step S10, acquiring sensor data collected in real time during the welding process, wherein the sensor data includes energy consumption, types of greenhouse gases emitted, and greenhouse gas emissions.

[0051] It should be noted that during the welding process, sensors can be used to monitor and record various data in the welding process in real time. The data detected by the sensors include welding temperature, humidity, voltage, current, etc., and may also include gas concentration, gas type and gas emission released by welding.

[0052] Greenhouse gases are gases that absorb and radiate long-wave infrared radiation in the Earth's atmosphere. In the welding process, greenhouse gas types include carbon dioxide, methane, nitrogen oxides, etc.

[0053] Energy consumption refers to the amount of energy used in the welding process, including electricity, natural gas, liquefied petroleum gas, etc. Energy consumption is used to evaluate the energy efficiency and environmental impact of the welding process.

[0054] Optionally, the sensor includes an energy consumption sensor, a temperature sensor, a gas analysis sensor, etc.

[0055] Optionally, sensors are installed in the welding work area to monitor the gas and energy usage generated during the welding process. For example, a gas sensor is installed in the welding workshop to monitor the emission of greenhouse gases such as carbon dioxide and nitrogen oxides, and an electric power sensor is installed to monitor the power consumption of the electric welder.

[0056] Optionally, sensors are installed on the welding equipment to collect data during the welding process in real time. For example, a power sensor is installed at the power input end of the welding equipment to monitor power consumption; a temperature sensor is installed near the welding head to monitor the temperature of the gas released during the welding process.

[0057] Optionally, the collected sensor data is uploaded to a cloud server in real time through IoT technology, wherein an IoT gateway is deployed to collect data from sensors, upload the data to a cloud server through a wireless network, a cellular network, or Ethernet, and store it in a database for further analysis.

[0058] Step S20: determining the direct carbon emissions during the welding process according to the greenhouse gas type and the greenhouse gas emissions.

[0059] It should be noted that direct carbon emissions refer to the carbon dioxide equivalents that can be detected in the welding process. Carbon dioxide equivalent is a unit used to measure the potential contribution of different greenhouse gases to global warming. The greenhouse effect of various greenhouse gases is compared with carbon dioxide and expressed as carbon dioxide equivalent, i.e., CO2e. Carbon dioxide equivalent is a unit for measuring greenhouse gas emissions. It can convert the global warming potential (GWP) of different greenhouse gases into carbon dioxide emissions, unify the impact of different gases on global warming, and facilitate comparison and analysis.

[0060] Optionally, greenhouse gases are directly emitted during some welding processes, and the direct carbon emissions during the welding process can be determined based on these greenhouse gases. Greenhouse gas detection and analysis is performed on each operation step in the welding process to determine the type and amount of greenhouse gas emissions emitted in each operation step, and then the emissions of each type of greenhouse gas are converted into carbon dioxide emissions and accumulated one by one to calculate the direct carbon emissions corresponding to the operation step. The direct carbon emissions corresponding to each operation step can also be accumulated one by one to calculate the direct carbon emissions generated by the entire welding process.

[0061] For example, during arc welding, the high temperature generated by the current passing through the electrode and the workpiece causes the oxygen in the surrounding air to react with the metal to generate carbon dioxide. At the same time, the nitrogen in the air reacts with the oxygen at high temperature to generate nitrogen oxides. The GWP of nitrogen oxides is converted into carbon dioxide emissions and added to the generated carbon dioxide emissions to determine the direct carbon emissions of the arc welding process.

[0062] Step S30, determining the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, wherein the carbon emission factors include carbon emissions per unit energy and carbon emissions per unit welding materials.

[0063] It should be noted that the carbon emission factor refers to the amount of greenhouse gas emitted per unit of energy consumption or per unit of material production, usually expressed in CO2e. Alternatively, greenhouse gas emission inventories, environmental statistics yearbooks, etc. usually provide carbon emission factors for various types of energy and greenhouse gases, and the carbon emission factor can be determined by consulting the information.

[0064] Optionally, the energy sample is subjected to laboratory analysis to determine its carbon emission factor, for example, the amount of carbon dioxide produced per ton of coal combustion is calculated based on the carbon content and combustion efficiency of the coal. Optionally, a neural network model is used to simulate the process of different energy sources and greenhouse gas emissions, and the carbon emission factor is determined based on the model results.

[0065] It should also be noted that indirect carbon emissions in the welding process refer to greenhouse gases emitted due to other consumption during welding operations, in addition to the carbon emissions directly generated by welding equipment, including greenhouse gases emitted during the production process of welding materials and energy consumed.

[0066] For example, when steel and aluminum come into contact with components in the air in a molten state, a series of reactions occur. The surface of molten steel can react with oxygen to produce iron oxides, such as FeO. At the same time, it may also react with water vapor in the air to produce hydrogen and other oxides. These reactions do not directly produce greenhouse gases such as carbon dioxide or methane, but greenhouse gases are emitted during the production of welding materials such as steel and aluminum. Both material production and welding processes require energy consumption, and energy production also emits greenhouse gases. These greenhouse gas emissions are counted as indirect carbon emissions in the welding process.

[0067] Alternatively, welding materials are consumed during welding, and the production of welding materials often involves multiple manufacturing processes such as material mining, smelting, and extrusion, which generate carbon emissions. The carbon emission factor of welding materials is determined by consulting data, that is, the carbon emissions generated by each unit of welding material production, and the indirect carbon emissions of welding material consumption are determined by multiplying the consumption of welding materials by their carbon emission factor.

[0068] For example, taking ER4043 welding wire as an example, based on the composition analysis and carbon emissions of the general production process of aluminum alloy welding wire, the carbon emission factor of ER4043 welding wire is calculated to be 25.815kg CO2e / kg ER4043 welding wire. When 10kg ER4043 welding wire is consumed as filler metal in a certain step of the welding process, the indirect carbon emissions generated are 10×25.815=258.15kg CO2e.

[0069] Optionally, a large amount of energy such as electricity, gas and fuel is consumed during welding. For example, arc welding requires a large amount of electricity to generate an arc, and gas cutting uses gases such as acetylene or propane to generate a high-temperature flame to cut metal. The carbon emission factor of the corresponding energy is determined by consulting the data, and the indirect carbon emissions of energy consumption are determined by multiplying the energy consumption by its carbon emission factor.

[0070] For example, an arc welder generates an arc and consumes electricity during the welding process. According to the data, the global average carbon emission factor of electricity is 0.5kg CO2e / kWh of electricity. Assuming that the sensor collects 100kWh of electricity during the welding process, the indirect carbon emissions generated by the electricity consumption during the welding process are 100×0.5=50kg CO2e.

[0071] Step S40, determining the total carbon emissions during the welding process according to the direct carbon emissions and the indirect carbon emissions.

[0072] The total carbon emissions in the welding process are determined by adding the indirect carbon emissions generated by each step of the welding process such as energy consumption and welding material consumption to the direct carbon emissions obtained by direct detection.

[0073] In this embodiment, by collecting sensor data in real time and comprehensively considering energy consumption, welding material consumption and corresponding carbon emission factors, the direct and indirect carbon emissions in the welding process can be calculated more accurately, thereby improving the accuracy of carbon emission determination.

[0074] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 In this embodiment, before step S30, steps A301 to A302 are also included:

[0075] Step A301, obtaining the energy type corresponding to the energy consumption.

[0076] Step A302: Determine the carbon emission factor according to the energy type.

[0077] Exemplarily, by identifying and recording the types of energy consumed during the welding process, including electricity, natural gas, propane or other fuels, after determining the energy type, the corresponding carbon emission factor is looked up or calculated to ensure accurate calculation of indirect carbon emissions during the welding process, thereby improving the accuracy of carbon emissions determination.

[0078] Based on the above embodiment, in the third embodiment of the present application, the same or similar contents as those in the above embodiment can be referred to the above introduction, and will not be described in detail later. Figure 3 In this embodiment, before step S30, steps B301 to B302 are also included:

[0079] Step B301, obtaining the material type of the welding material.

[0080] Step B302: determining the carbon emission factor according to the material type.

[0081] Optionally, based on reports published by the International Energy Agency and other organizations, as well as other relevant life cycle assessment studies, the main chemical composition of the welding materials and the carbon emission factor corresponding to each component element in the welding materials are determined, the CO2e emissions of each element are calculated, and then the carbon emission factor, i.e. the carbon emissions generated per unit of welding material production, is calculated by weighted average based on the chemical composition of the welding materials.

[0082] Exemplarily, the welding tool of friction stir welding is the core component of welding technology. The welding tool is mainly composed of a stirring pin and a shoulder, which act together on the material to be welded. Take the stirring pin made of tungsten carbide-based alloy as an example, where the tungsten carbide-based alloy is mainly composed of 88% tungsten carbide, 10% cobalt, 1% iron, and other elements (including tantalum, niobium, etc.).

[0083] According to the published tungsten production life cycle assessment (LCA) study, the carbon emission factor of tungsten carbide is in the range of 5-10kgCO2e / kg tungsten carbide. For specific calculations, considering the average energy consumption and emissions of tungsten mining, transportation, refining and carburization processes, 9kg CO2e / kg tungsten carbide can be selected as the calculated carbon emission factor value of tungsten carbide element. According to the environmental impact assessment of the cobalt supply chain or the LCA study of cobalt mining and processing, the carbon emission factor of cobalt is estimated to be 10-20kg CO2e / kg cobalt, and 15kg CO2e / kg Co is selected as the calculated carbon emission factor value of the cobalt element. According to the environmental report of the steel industry and the LCA analysis of steel production, the carbon emission factor of iron is about 1.8-2.2kgCO2e / kg iron. Considering the conversion process from iron ore to pure iron, 2kg CO2e / kg Fe is selected as the calculated carbon emission factor value of iron element. Other elements have lower content and complex mining and processing processes. It is assumed that their CO2e emissions are slightly higher than conventional metals. For example, 3kg CO2e / kg material is selected as the calculated value of the carbon emission factor for other elements.

[0084] Determine the carbon emission factor of tungsten carbide based alloy based on the above calculated value:

[0085] Tungsten carbide (88%): 0.88×9=7.92kg CO2e;

[0086] Cobalt (10%): 0.10×15=1.5 kg CO2e;

[0087] Iron (1%): 0.01×2=0.02kg CO2e;

[0088] Other elements (1%): 0.01×3=0.03kg CO2e;

[0089] A total of 9.47 kg CO2e / kg tungsten carbide-based alloy.

[0090] Optionally, the carbon emission factor of the welding material is determined based on the energy consumption in the production process of the welding material.

[0091] For example, the processing, molding, heat treatment and surface treatment of the stirring needle consume energy and generate carbon emissions. Assuming that the production of a stirring needle requires a total of 8 hours of machining time, consuming about 15kWh of electricity per hour, assuming that the heat treatment process takes 2 hours, the energy consumption is about 50kWh per hour, and assuming that the energy consumption of surface treatment is relatively small, it is estimated to be 5% of the total energy consumption. The global average electricity carbon emission factor of 0.5kg CO2e / kWh is used as the calculation value.

[0092] The carbon emission factor of a single stirring needle is determined based on the above calculated values:

[0093] The processing energy consumption is 8×15=120kWh;

[0094] The energy consumption of heat treatment is 2×50=100kWh;

[0095] The energy consumption of surface treatment is (120kWh+100kWh)×5%≈11kWh;

[0096] Then the total energy consumption is 120kWh+100kWh+11kWh=231kWh;

[0097] A total of 231 x 0.5 = 115.5 kg CO2e / stirring needle.

[0098] It should be noted that the specific calculated value of the carbon emission factor can be customized according to different determination methods, and this application does not impose any restrictions on this value and its determination method.

[0099] In this embodiment, the carbon emission factor of the welding material is determined by identifying the type of welding material, and then the indirect carbon emissions of the welding process are calculated, ensuring that the calculation of the carbon emissions of the welding process is accurate and comprehensive.

[0100] Based on the above embodiments, in the fourth embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 4 In this embodiment, the method for determining carbon emissions further includes steps S11 to S21:

[0101] Step S11, extracting characteristic data related to carbon emissions from the sensor data, wherein the characteristic data affects the size of carbon emissions;

[0102] It should be noted that characteristic data are factors that affect carbon emissions. Characteristic data include energy consumption peak, temperature change in welding area and welding parameters, among which welding parameters include current, voltage, welding speed, etc.

[0103] It is understandable that unreasonable welding parameter settings will lead to increased energy consumption in the welding process. For example, excessive current or voltage will cause short circuit or arc instability, resulting in a sharp increase in carbon emissions. Analyzing these characteristic data can display the carbon emissions in the welding process in more detail, identify the causes of increased carbon emissions, and optimize them.

[0104] Step S21, generating a carbon emission report during the welding process according to the characteristic data.

[0105] Optionally, a carbon emission report for the welding process is generated by calculating the carbon emissions and characteristic data. The carbon emission report records the carbon emissions during the welding process in detail, including the carbon emissions, the main sources of carbon emissions, the time distribution of carbon emissions, etc.

[0106] Optionally, the characteristic data can be used to predict carbon emissions from future welding activities and generate a carbon emissions report.

[0107] For example, an enterprise can use machine models such as linear regression models to predict carbon emissions within a preset time in the future, or the carbon emissions that will be generated by producing preset products, based on current carbon emissions data and characteristic data.

[0108] Optionally, apply advanced data visualization techniques such as dynamic dashboards and real-time updated charts to present carbon emissions reports of the welding process.

[0109] In this embodiment, by analyzing the characteristic data during the welding process, analyzing and predicting the carbon emissions during the welding process, and generating a carbon emission report, the changes in various parameters during the welding process and the carbon emissions can be understood more directly.

[0110] Based on the above embodiments, in the fifth embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 5 , step S11 may include steps C111 to C113:

[0111] Step C111, performing time series analysis on the sensor data to generate time series data;

[0112] It should be noted that time series analysis is a statistical method used to process data that changes over time. During the welding process, sensor data such as temperature, current, voltage, etc. change over time. By performing time series analysis on sensor data, time series data can be generated. Time series data is data points arranged in chronological order, reflecting the changing trend of various parameters over time during the welding process.

[0113] Optionally, through time series analysis, statistical features such as mean, standard deviation, maximum and minimum values ​​of different time windows are generated, and short-term and long-term trends are captured through rolling window features.

[0114] Step C112, determining the correlation between the time series data and the carbon emissions;

[0115] Optionally, the correlation between two variables is calculated by a correlation algorithm, such as Pearson's correlation coefficient. Optionally, the importance of features is evaluated by an importance evaluation algorithm, such as calculating the importance of features in a random forest.

[0116] Step C113, determining the characteristic data according to the correlation.

[0117] It is understandable that if there is a strong correlation between the time series data and carbon emissions, it means that these data can reflect the carbon emissions in the welding process. If there is a weak correlation between the time series data and carbon emissions, it means that these data contribute less to the analysis of carbon emissions.

[0118] Optionally, the Pearson correlation coefficient between the time series data and the carbon emissions is calculated, and the time series data with a correlation coefficient greater than a preset coefficient is used as feature data.

[0119] Optionally, in random forests, feature importance is measured by evaluating the contribution of each feature to model prediction when building a decision tree. Train a random forest model with time series data as features and carbon emissions as the target variable. During training, record the importance score of each feature, and use features with importance scores greater than a preset score as feature data.

[0120] Exemplarily, when the feature of average current during welding is frequently used as a split feature when constructing multiple decision trees, it indicates that the importance of this feature is high. When the number of times this feature is used as a split feature exceeds a preset number, such as 20 times, the feature is determined as feature data.

[0121] In this embodiment, the time series data generated by time series analysis, combined with the correlation algorithm and the importance assessment algorithm, can effectively determine which feature data has a high correlation with carbon emissions, so as to find out the feature data that affects carbon emissions and improve the accuracy of feature extraction.

[0122] Based on the above embodiments, in the sixth embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above introduction, and will not be repeated later. On this basis, after step S50, it also includes: according to the characteristic data and the carbon emissions, training a preset neural network model to obtain a prediction model, and the prediction model is used to predict the carbon emissions during the welding process.

[0123] It should be noted that the preset neural network model includes multiple layers of neurons and connections, as well as a defined learning algorithm. The weights and biases of the neural network model are adjusted for training through feature data and corresponding carbon emissions. During the training process, the neural network model minimizes the difference between the predicted value and the actual carbon emissions by continuously learning the patterns in the data. After the training is completed, the neural network model is transformed into a prediction model that can receive new feature data as input and output predicted carbon emissions.

[0124] Optionally, nonlinear features are extracted from the sensor data through a preset variational autoencoder model; and based on the nonlinear features, the variational autoencoder model is optimized through a loss function and / or a gradient descent algorithm.

[0125] It should be noted that the Variational Autoencoder (VAE) is a deep learning model, including a decoder and an encoder, which is used to reduce the dimension of the collected sensor data, reduce or remove redundant information in the sensor data, reduce computational complexity, and improve the efficiency of model training. There are characteristic data in the sensor data that have a complex nonlinear relationship with carbon emissions. For example, there is a complex nonlinear relationship between temperature and carbon emissions during welding. Too high temperature will cause the material to overburn, thereby increasing carbon emissions, while too low temperature may cause the welding quality to deteriorate and require re-welding, which will also increase carbon emissions. By learning the potential representation of this data, VAE can automatically discover and extract nonlinear features in sensor data, capture more complex characteristic factors in the data that lead to carbon emissions, and improve prediction accuracy.

[0126] Optionally, the preset VAE model encodes the sensor data and converts the sensor data into low-dimensional latent variables. These latent variables contain nonlinear features in the sensor data, such as the temperature change trend and energy consumption fluctuation during welding.

[0127] Exemplarily, the sensor data collected during the welding process include current, voltage, temperature, welding speed and welding time. These sensor data are input into the encoder of the VAE model. The encoder performs nonlinear transformation on the sensor data, extracts nonlinear features and generates low-dimensional latent variables such as the average temperature, average current and energy consumption trend during the welding process.

[0128] Optionally, according to the nonlinear characteristics, the variational autoencoder model is optimized by a loss function and / or a gradient descent algorithm.

[0129] It should be noted that the loss function is an indicator that measures the difference between the model's predicted value and the true value. In the VAE model, two loss functions are usually used: reconstruction loss and KL divergence: reconstruction loss is used to measure the difference between the decoder output and the original input data, such as mean square error; KL divergence is used to measure the difference between the latent variable distribution and the standard normal distribution. The smaller the KL divergence, the closer the latent variable distribution is to the standard normal distribution, and the stronger the model's expressive power is.

[0130] Optionally, the difference between the predicted value and the true value of the VAE model is measured according to a preset loss function, and the parameters of the VAE model are updated by a gradient descent algorithm to minimize the value of the loss function and improve the accuracy of feature data extraction.

[0131] Exemplarily, before the step of extracting nonlinear features from the sensor data, the parameters of the VAE model, including the weights and biases of the encoder and decoder, are initialized. The latent variables are input into the decoder for decoding, reconstructed data is generated, and the sum of the reconstruction loss and the KL divergence is calculated to obtain the total loss function value. Then, the gradient of the loss function with respect to the model parameters is calculated, and the model parameters are updated using the gradient descent algorithm, and the above steps of decoding to updating the model parameters are repeated to minimize the value of the loss function.

[0132] In this embodiment, a neural network model is trained by using characteristic data and corresponding carbon emissions to learn how to predict carbon emissions during welding, thereby helping to monitor and control carbon emissions during actual welding operations.

[0133] Based on the above embodiments, in the seventh embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 6 , after step S21, further comprising steps S31 to S51:

[0134] Step S31, determining, based on the carbon emission report, target characteristic data corresponding to the carbon emission being greater than a preset threshold.

[0135] It should be noted that the preset threshold is a standard or limit set in advance to determine whether the carbon emissions are too high. If the actual measured or calculated carbon emissions exceed this threshold, it is considered that the carbon emissions of this welding operation are not ideal, and the characteristic data directly related to the carbon emissions exceeding the threshold are found for further analysis and optimization.

[0136] Step S41, determining welding parameters to be adjusted and / or process flow to be improved according to the target characteristic data.

[0137] It should be noted that the welding parameters to be adjusted are specific parameters that can be changed during the welding process, including welding current, voltage, welding speed, welding material type, etc. The purpose of adjusting these parameters is to find the best settings that can reduce carbon emissions. The process to be improved refers to the steps and methods of the entire welding operation, including material preparation, welding technology, post-processing, etc. Improving the process includes changing the welding sequence, using more efficient welding technology, or introducing new welding equipment.

[0138] Step S51, generating an adjustment strategy according to the welding parameters to be adjusted and / or the process flow to be improved.

[0139] Optionally, the adjustment strategy may include specific steps, timelines, resource requirements, and expected results.

[0140] For example, suppose that when analyzing the carbon emission report of welding operations, it is found that the size of the welding current has a significant positive correlation with the carbon emissions. Assuming that the set carbon emission threshold is 100kgCO2, through further analysis, it is found that when the welding current exceeds 150A, the carbon emissions will exceed the threshold, so the target characteristic data is determined to be the current of 150A. Based on the target characteristic data, the main improvement direction can be determined: reduce the welding current setting from 150A to 120A; use more efficient welding technology during the welding process, such as laser welding or plasma welding, to achieve the same welding effect at a lower current. Finally, the above improvement directions are generated into adjustment strategies.

[0141] In this embodiment, the target characteristic data is determined by analyzing the carbon emission report, and the welding parameters to be adjusted and / or the process flow to be improved are determined accordingly, and then an adjustment strategy is generated. This can better predict and control carbon emissions in the welding process, help formulate more effective emission reduction measures, and help enterprises achieve sustainable development goals.

[0142] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for determining carbon emissions of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0143] The present application provides a device for determining carbon emissions, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining carbon emissions in the above-mentioned embodiment one.

[0144] Reference below Figure 7 , which shows a schematic diagram of the structure of a device for determining carbon emissions suitable for implementing the embodiment of the present application. The device for determining carbon emissions in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7The carbon emission determination device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0145] like Figure 7 As shown, the carbon emission determination device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the carbon emission determination device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the carbon emission determination device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a carbon emission determination device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0146] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0147] The carbon emission determination device provided by the present application adopts the carbon emission determination method in the above embodiment, which can solve the technical problem of low accuracy in carbon emission determination. Compared with the prior art, the beneficial effects of the carbon emission determination device provided by the present application are the same as the beneficial effects of the carbon emission determination method provided by the above embodiment, and the other technical features in the carbon emission determination device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0148] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0149] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0150] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for determining carbon emissions in the above-mentioned embodiment.

[0151] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0152] The computer-readable storage medium may be included in the device for determining carbon emissions; or may exist independently without being incorporated into the device for determining carbon emissions.

[0153] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the carbon emission determination device, the carbon emission determination device can write computer program codes for performing the operations of the present application in one or more programming languages ​​or a combination thereof. The programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0154] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0156] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for determining carbon emissions, and can solve the technical problem of low accuracy in determining carbon emissions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for determining carbon emissions provided in the above-mentioned embodiment, and will not be elaborated here.

[0157] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for determining carbon emissions when executed by a processor.

[0158] The computer program product provided by the present application can solve the technical problem of low accuracy in determining carbon emissions. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the method for determining carbon emissions provided by the above embodiment, which will not be described in detail here.

[0159] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for determining carbon emissions, characterized in that: The method comprises: Acquire sensor data collected in real time during the welding process, wherein the sensor data includes energy consumption, types of greenhouse gases emitted, and greenhouse gas emissions; determining direct carbon emissions from the welding process based on the greenhouse gas types and the greenhouse gas emissions; Determine the indirect carbon emissions in the welding process according to the energy consumption, welding materials and carbon emission factors, wherein the carbon emission factors include carbon emissions per unit of energy and carbon emissions per unit of welding materials; The total carbon emissions during the welding process are determined according to the direct carbon emissions and the indirect carbon emissions.

2. The method according to claim 1, characterized in that Before the step of determining the indirect carbon emissions during welding based on the energy consumption, welding materials and carbon emission factors, the step further includes: Obtaining the energy type corresponding to the energy consumption; The carbon emission factor is determined based on the energy type.

3. The method according to claim 1, characterized in that Before the step of determining the indirect carbon emissions during welding based on the energy consumption, welding materials and carbon emission factors, the step further includes: Obtaining the material type of the welding material; The carbon emission factor is determined based on the material type.

4. The method according to claim 1, characterized in that The method further comprises: Extracting characteristic data related to carbon emissions from the sensor data, wherein the characteristic data affects the magnitude of carbon emissions; A carbon emission report for the welding process is generated according to the characteristic data and the carbon emission amount.

5. The method according to claim 4, characterized in that After the step of generating a carbon emission report in the welding process according to the characteristic data and the carbon emission amount, the method further includes: Determine, according to the carbon emission report, that the carbon emission amount is greater than the target characteristic data corresponding to the preset threshold; Determining welding parameters to be adjusted and / or process flow to be improved according to the target characteristic data; An adjustment strategy is generated according to the welding parameters to be adjusted and / or the process to be improved.

6. The method according to claim 4, characterized in that After the step of extracting characteristic data related to carbon emissions from the sensor data, the method further comprises: According to the characteristic data and the carbon emissions, a preset neural network model is trained to obtain a prediction model, and the prediction model is used to predict the carbon emissions during the welding process.

7. The method according to claim 4, characterized in that The step of extracting characteristic data related to carbon emissions from the sensor data further includes: Performing time series analysis on the sensor data to generate time series data; Determining a correlation between the time series data and the carbon emissions; The characteristic data is determined according to the correlation.

8. A device for determining carbon emissions, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining carbon emissions according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for determining carbon emissions according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for determining carbon emissions according to any one of claims 1 to 7 are implemented.

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