Industrial mechanical equipment carbon emission calculation method based on vibration data

Abstract

The invention discloses a vibration data-based industrial mechanical equipment carbon emission calculation method, which comprises the steps of collecting a vibration signal through an MPU6050 sensor, extracting characteristic parameters such as a vibration amplitude mean value, frequency distribution and energy density, establishing an operation state and energy consumption mapping model in combination with a support vector regression algorithm, and calculating the carbon emission of industrial mechanical equipment. And accurate calculation is realized by using a carbon emission factor and a dynamic correction coefficient. According to the method, the carbon emission of the equipment can be monitored in real time in a non-intrusive mode, installation and maintenance are simplified, calculation precision is improved, a scientific basis is provided for carbon emission management of an engineering construction site, meanwhile, energy conservation and emission reduction optimization design of the mechanical equipment is assisted, and the method has important application value and social significance.

Description

Technical Field

[0001] The present invention relates to the field of industrial equipment monitoring and environmental protection technology, and in particular to a method for calculating carbon emissions of industrial machinery equipment based on vibration data. Background Art

[0002] The global push for "carbon peak" and "carbon neutrality" policies marks a new phase in society's understanding of climate change. To achieve this goal, emissions monitoring and management of industrial machinery and equipment, a significant source of carbon emissions, have become crucial. However, current technologies for measuring carbon emissions from machinery and equipment still have numerous shortcomings and cannot fully meet practical needs.

[0003] In the existing technology, common carbon emission monitoring equipment includes fuel tank level gauges, industrial sulfur hexafluoride alarms, and carbon emission monitoring analyzers. These devices can provide relevant data on the operating status or emissions of mechanical equipment to a certain extent, but they generally have the problem of invasive installation. For example, some devices need to be directly connected to the fuel system or exhaust pipe of the mechanical equipment. This installation method not only increases the complexity of equipment modification, but may also interfere with the normal operation of the mechanical equipment. In addition, these devices usually rely on external power supply and are difficult to operate for a long time in scenarios without power supply, resulting in their scope of application being limited, especially in remote areas or construction sites with harsh construction environments. The high installation cost and maintenance costs further restrict the popularization and promotion of such equipment.

[0004] To address these issues, an innovative solution is urgently needed that can achieve real-time, refined monitoring of carbon emissions from mechanical equipment without changing its original structure. At the same time, this solution also needs to be able to operate for extended periods without an external power source to accommodate a variety of complex application scenarios. Against this backdrop, this paper proposes a method for calculating carbon emissions from industrial machinery based on vibration data. This method addresses the shortcomings of traditional technologies by constructing an automated carbon monitoring system consisting of an information collection module, an energy supply module, and a cloud-based Internet of Things platform. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the invention aims to solve the problems of inconvenient invasive installation, insufficient energy supply and low monitoring accuracy in the existing technology by introducing an intelligent monitoring method based on vibration data, combined with efficient energy management and cloud data analysis technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for calculating carbon emissions of industrial machinery equipment based on vibration data includes the following steps: S1. The vibration data of the mechanical equipment in the XYZ directions is acquired through the information acquisition module and converted into analog signal output. The vibration data is collected using the MPU6050 three-dimensional angular velocity sensor, and the sampling frequency is dynamically adjusted according to the operating status of the equipment; S2. The collected vibration data is transmitted to the post-processing unit through a unified data interface for signal filtering, denoising and feature extraction to obtain key characteristic parameters reflecting the operating status of the equipment, including the mean vibration amplitude. , vibration frequency distribution and vibration energy density ; S3. Establish a mapping relationship model between the operating status of mechanical equipment and energy consumption. The model is built based on historical data and machine learning algorithms, and the input is vibration characteristic parameters. 、 and , the output is the energy consumption of the device per unit time ; S4. Calculate the carbon emissions of mechanical equipment by combining equipment energy consumption and carbon emission factors. Determined by the type of energy used by the equipment; S5. Introducing dynamic correction coefficient Correct carbon emissions, dynamic correction coefficient The calculation formula includes the weights of the influencing factors and the response values; S6. Upload the calculated carbon emission data to the cloud IoT platform for real-time display and statistical analysis.

[0007] Optionally, the vibration amplitude mean The calculation formula is: , where Indicates the The vibration amplitude of the sampling point, Indicates the total number of sampling points.

[0008] Optionally, the vibration frequency distribution The calculation formula is: , where represents the frequency component index, Indicates the The vibration amplitude of each sampling point.

[0009] Optionally, the vibration energy density The calculation formula is: , where and Represent the lower and upper limits of the frequency range respectively.

[0010] Optionally, the mapping relationship model between the operating status of the mechanical equipment and the energy consumption is constructed using a support vector regression algorithm, and the objective function is: , the constraints are, , where and denote the hyperplane weight and bias respectively, represents the nonlinear mapping function of the input features, Indicates the actual energy consumption value, Indicates the allowable error range. and represents the slack variable, represents the regularization parameter.

[0011] Optionally, carbon emissions The calculation formula is: , where It represents the energy consumption per unit time, Indicates the corresponding carbon emission factor.

[0012] Alternatively, for multi-energy hybrid-driven equipment, the carbon emissions calculation formula is: , where Indicates the number of energy types, and Respectively represent The energy consumption per unit time and carbon emission factor of each energy source.

[0013] Optionally, a dynamic correction factor The calculation formula is:

[0014] Where, represents the number of influencing factors, and Respectively represent The weight and response value of each influencing factor.

[0015] Optionally, the cloud IoT platform adopts a hierarchical management model, supports the display of carbon emissions of individual carbon monitoring devices, projects and enterprises, and supports OTA online upgrade functions; The MPU6050 three-dimensional angular velocity sensor in the information acquisition module is installed at the key position of the mechanical equipment to collect vibration signals in the XYZ directions. The sensor sampling frequency can be dynamically adjusted according to the actual operating status of the equipment.

[0016] In addition, to achieve the above-mentioned purpose, the present invention further proposes a monitoring device, which is used to read and execute the calculation method, and is provided with a solar cell component for providing electricity.

[0017] Beneficial effects The vibration data-based carbon emissions calculation method for industrial machinery and equipment proposed in this invention fully utilizes the vibration signals generated during the operation of the machinery and equipment, and combines the energy consumption model and carbon emission factor to achieve accurate calculation of carbon emissions. This method does not require invasive modifications to the equipment, simplifying installation and maintenance operations. At the same time, the solar power supply design ensures the long-term stable operation of the system. In addition, the introduction of a dynamic correction coefficient further improves the calculation accuracy, making the carbon emissions monitoring results more reliable. This invention not only provides a scientific basis for carbon emissions management at construction sites, but also provides theoretical support for the energy-saving and emission-reduction optimization design of machinery and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a system architecture diagram of the method of the present invention; Figure 2 This is a flow chart of vibration data processing according to the present invention; Figure 3 Schematic diagram of the monitoring device of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] The present invention provides a technical solution: a method for calculating carbon emissions of industrial machinery and equipment based on vibration data. The core of the method is to collect vibration signals of mechanical equipment during operation, combine the equipment operation status and energy consumption model, and achieve real-time monitoring and accurate calculation of carbon emissions. Figure 1 and attached Figure 2 Specific embodiments of the present invention are described in detail.

[0021] First, the setting of the information acquisition module and the acquisition process of vibration data are described in the attached figure. Figure 1As shown, the information acquisition module consists of an MPU6050 three-dimensional angular velocity sensor, installed in key locations on the mechanical equipment to collect vibration signals in the X, Y, and Z directions. The sensor sampling frequency can be dynamically adjusted based on the equipment's actual operating status. For example, a lower sampling frequency can be set under low-load conditions to reduce data redundancy, while a higher sampling frequency can be used to ensure data accuracy under high-load or complex conditions. The vibration signal is processed by the sensor's internal analog-to-digital converter and output as an analog signal, which is transmitted to the post-processing unit via a unified data interface. During this process, vibration data acquisition must meet high accuracy and low noise interference requirements. Therefore, in practical applications, regular sensor calibration is required, and appropriate filtering parameters must be selected based on the equipment's operating environment.

[0022] Next, enter the vibration data processing process, as shown in the attached Figure 2 As shown. The original vibration signal received by the post-processing unit first undergoes preprocessing steps, including signal filtering and denoising operations. Signal filtering uses a low-pass filter to remove high-frequency noise while retaining effective vibration information in the low-frequency band; denoising uses a wavelet transform algorithm to suppress random noise in the signal to improve the accuracy of subsequent feature extraction. On this basis, key characteristic parameters that can reflect the operating status of the equipment are extracted, including the mean vibration amplitude , vibration frequency distribution and vibration energy density Among them, the mean vibration amplitude The calculation formula is: , where Indicates the The vibration amplitude of the sampling point, Indicates the total number of sampling points. This formula takes the absolute value of the vibration amplitude of all sampling points and calculates the average value to obtain the overall level of vibration intensity during equipment operation. Vibration frequency distribution It is obtained by the fast Fourier transform (FFT) algorithm, and its expression is: , where represents the frequency component index, Indicates the The vibration amplitude of each sampling point. By converting the time domain signal into the frequency domain signal through the FFT algorithm, the energy distribution of different frequency components during the operation of the equipment can be analyzed. Finally, the vibration energy density The formula is obtained by integral calculation: , where and Representing the lower and upper limits of the frequency range, respectively. This formula quantifies the total amount of vibration energy generated during equipment operation by integrating the vibration energy within a specific frequency range. The extraction of these characteristic parameters effectively reduces data redundancy while retaining key information, laying the foundation for subsequent carbon emissions calculations.

[0023] After completing the vibration data processing, the mapping relationship modeling phase between the mechanical equipment operating status and energy consumption is entered. Figure 1 As shown, the model is built based on historical data and machine learning algorithms, with the input being vibration characteristic parameters. 、 and , the output is the energy consumption of the device per unit time Specifically, the support vector regression (SVR) algorithm is used to model the mapping relationship, and its objective function is: , the constraints are: , where and denote the hyperplane weight and bias respectively, represents the nonlinear mapping function of the input features, Indicates the actual energy consumption value, Indicates the allowable error range. and represents the slack variable, represents the regularization parameter. The trained model can accurately predict the energy consumption of equipment under different operating conditions. In practical applications, a large amount of historical data is required as training samples, including vibration characteristic parameters and corresponding energy consumption values ​​during equipment operation. This data can be obtained through experimental testing or field monitoring, preprocessed, and then input into the SVR model for training. After training, the model can predict the equipment's energy consumption based on the real-time vibration characteristic parameters, providing a basis for carbon emissions calculation.

[0024] Get the energy consumption of the equipment per unit time Finally, the carbon emissions of mechanical equipment are calculated by combining the energy consumption of the equipment with the carbon emission factor. Determined by the type of energy used by the equipment, for example, the carbon emission factor of a diesel engine is , the carbon emission factor of the electric motor is Carbon emissions The calculation formula is: Where, It represents the energy consumption per unit time, Indicates the corresponding carbon emission factor. For multi-energy hybrid driven equipment, carbon emissions can be calculated using the weighted average method, and the formula is: , where Indicates the number of energy types, and Respectively represent The energy consumption per unit time and carbon emission factor of each energy source. In actual application, the proportion of each energy source must be determined based on the specific energy usage of the equipment and calculated in combination with the corresponding carbon emission factor. For example, if a device uses both diesel and electricity as energy sources, its diesel energy consumption accounts for 60% of the total energy consumption and electricity energy consumption accounts for 40%. The carbon emission calculation formula is: .

[0025] In order to further improve the calculation accuracy, the dynamic correction coefficient is introduced Correct carbon emissions. Dynamic correction factor The calculation formula is: , where represents the number of influencing factors, and Respectively represent The weight and response value of each influencing factor. Influencing factors include ambient temperature, humidity, equipment load rate, etc. For example, in a certain application scenario, the influence weight of ambient temperature is 0.4, the influence weight of humidity is 0.3, and the influence weight of equipment load rate is 0.3. The dynamic correction coefficient It can be calculated by the following formula: , where represents the normalized response value of the ambient temperature, represents the normalized response value of humidity, Indicates the normalized response value of the equipment load rate. The introduction of can effectively compensate for the impact of external environmental changes on carbon emissions calculation, making the results closer to actual working conditions. The final carbon emissions calculation formula is: , where Indicates carbon emissions after dynamic correction.

[0026] Finally, the calculated carbon emission data is uploaded to the cloud IoT platform for real-time display and statistical analysis. Figure 1As shown, the cloud-based IoT platform adopts a hierarchical management model, supporting the display of carbon emissions for individual carbon monitoring devices, projects, and enterprises. The platform receives data from each monitoring device via wireless communication modules and stores, analyzes, and visualizes it. Users can view a device's real-time carbon emissions, historical trends, and comparative analysis results with other devices through the platform interface. Furthermore, the platform supports over-the-air (OTA) updates, enabling expansion and updating of device type and power identification models to accommodate the monitoring needs of a wider range of mechanical equipment. For example, at a construction site, carbon emissions data from multiple mechanical equipment is uploaded to the platform in real time. Managers can generate carbon emission reports and optimize equipment operation strategies based on the data analysis to achieve energy conservation and emission reduction goals. The system also incorporates an energy supply module consisting of a rechargeable lithium-ion battery, solar panels, and control circuitry. The solar cells provide real-time power to the device when there is sufficient sunlight, while storing excess energy in the lithium battery, enabling continuous operation for over a year without an external power source. This design ensures the system's continued stable operation even under the harsh conditions of the project site.

[0027] In summary, the method for calculating carbon emissions of industrial machinery and equipment based on vibration data proposed in the present invention makes full use of the vibration signals during the operation of the machinery and equipment, and combines the energy consumption model and the carbon emission factor to achieve accurate calculation of carbon emissions. This method does not require invasive modifications to the equipment, simplifies installation and maintenance operations, and at the same time ensures the long-term stable operation of the system through solar power supply design. In addition, the introduction of the dynamic correction coefficient further improves the calculation accuracy, making the monitoring results of carbon emissions more reliable. The present invention not only provides a scientific basis for carbon emission management at construction sites, but also provides theoretical support for the energy-saving and emission-reduction optimization design of machinery and equipment, and has important application value and social significance.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0029] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating carbon emissions from industrial machinery and equipment based on vibration data, characterized by: The following steps are involved: S1. The vibration data of the mechanical equipment in the XYZ directions is acquired through the information acquisition module and converted into analog signal output. The vibration data is collected using the MPU6050 three-dimensional angular velocity sensor, and the sampling frequency is dynamically adjusted according to the operating status of the equipment; S2. The collected vibration data is transmitted to the post-processing unit through a unified data interface for signal filtering, denoising and feature extraction to obtain key characteristic parameters reflecting the operating status of the equipment, including the mean vibration amplitude. , vibration frequency distribution and vibration energy density ; S3. Establish a mapping relationship model between the operating status of mechanical equipment and energy consumption. The model is built based on historical data and machine learning algorithms, and the input is vibration characteristic parameters. 、 and , the output is the energy consumption of the device per unit time ; S4. Calculate the carbon emissions of mechanical equipment by combining equipment energy consumption and carbon emission factors. Determined by the type of energy used by the equipment; S5. Introducing dynamic correction coefficient Correct carbon emissions, dynamic correction coefficient The calculation formula includes the weights of the influencing factors and the response values; S6. Upload the calculated carbon emission data to the cloud IoT platform for real-time display and statistical analysis.

2. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: Mean vibration amplitude The calculation formula is: , where Indicates the The vibration amplitude of each sampling point, Indicates the total number of sampling points.

3. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: Vibration frequency distribution The calculation formula is: , where represents the frequency component index, Indicates the The vibration amplitude of each sampling point.

4. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: Vibration energy density The calculation formula is: , where and Represent the lower and upper limits of the frequency range respectively.

5. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: The mapping relationship model between the operating status of mechanical equipment and energy consumption is constructed using the support vector regression algorithm, and the objective function is: , the constraints are, , where and denote the hyperplane weight and bias respectively, represents the nonlinear mapping function of the input features, Indicates the actual energy consumption value, Indicates the allowable error range. and represents the slack variable, represents the regularization parameter.

6. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: Carbon emissions The calculation formula is: , where It represents the energy consumption per unit time, Indicates the corresponding carbon emission factor.

7. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 6, wherein: For multi-energy hybrid-driven equipment, the carbon emissions calculation formula is: , where Indicates the number of energy types, and Respectively represent The energy consumption per unit time and carbon emission factor of each energy source.

8. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: Dynamic correction factor The calculation formula is: , where represents the number of influencing factors, and Respectively represent The weight and response value of each influencing factor.

9. The method for calculating carbon emissions of industrial machinery and equipment based on vibration data according to claim 1, wherein: The cloud IoT platform adopts a hierarchical management model, supports the display of carbon emissions of individual carbon monitoring devices, projects and enterprises, and supports OTA online upgrade functions; The MPU6050 three-dimensional angular velocity sensor in the information acquisition module is installed at the key position of the mechanical equipment to collect vibration signals in the XYZ directions. The sensor sampling frequency can be dynamically adjusted according to the actual operating status of the equipment.

10. A monitoring device used in the method for calculating carbon emissions of industrial machinery equipment based on vibration data according to any one of claims 1 to 9, characterized in that: The monitoring device is used to read and execute the calculation method to implement the method according to any one of claims 1 to 9, and a solar component for providing electricity is provided on the monitoring device.

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