A carbon emission measurement and monitoring device and method

Abstract

The present invention discloses a carbon emission metering monitoring device and method, which relates to the technical field of carbon emission metering, and the device includes: a reference information acquisition module for collecting the equipment status, energy composition and working environment information of the target equipment during operation; an adaptive fusion module generates fused information by analyzing the floating ratio of the equipment status, energy composition and working environment information to the standard information, combined with historical data of different time ranges; an accuracy evaluation module evaluates the accuracy of carbon emission metering based on the fused data; an emission metering module inputs the monitored energy usage data into the corresponding channel of the carbon emission meter based on the accuracy information, performs carbon emission metering, and generates carbon emission metering results. The present invention can enable the hot and cold carbon meter to not only convert the hot and cold metering into carbon emissions, but also perform carbon emission metering based on the operating characteristics, thereby achieving the technical effect of improving the monitoring accuracy and responsiveness and providing traceability monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission measurement, and in particular to a carbon emission measurement and monitoring device and method. Background Art

[0002] As the global climate change problem becomes increasingly serious, accurate carbon emission measurement and monitoring is the basis for formulating emission reduction strategies and achieving carbon neutrality goals. In industrial production, hot and cold carbon meters measure cold and heat through the measurement of two temperature ranges, and convert them into carbon emissions, so as to obtain a more comprehensive picture of carbon emissions and the combustion efficiency of emission sources. However, the existing carbon emission measurement methods are mostly based on the established carbon emission factors to measure carbon emissions from the obtained emission data, which makes it difficult to achieve local deployment and measurement monitoring, and there are technical problems such as insufficient monitoring accuracy, poor real-time performance, and lack of traceability. Summary of the invention

[0003] The present invention provides a carbon emission metering monitoring device and method to solve the technical problems of insufficient monitoring accuracy, poor real-time performance and lack of traceability in the prior art, thereby improving monitoring accuracy and responsiveness and providing the technical effect of traceability monitoring.

[0004] In a first aspect, the present invention provides a carbon emission measurement and monitoring device, wherein the device comprises:

[0005] The reference information acquisition module is used to collect device status information, energy composition information and working environment information during the operation of the target device to be carbon emission measured.

[0006] An adaptive fusion module is used to analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past to obtain fused device status information, fused energy composition information and fused working environment information.

[0007] The accuracy evaluation module is used to classify and obtain the current measurement accuracy information of carbon emission measurement for the target device according to the fusion device status information, fusion energy composition information and fusion working environment information.

[0008] An emission metering module, wherein the emission metering module is used to continuously monitor and obtain energy usage data during the operation of the target equipment, and input the energy usage data into the corresponding carbon emission metering channel in the carbon emission meter according to the metering accuracy information, to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, an integrated metering quantity is set to perform carbon emission metering.

[0009] In a second aspect, the present invention further provides a carbon emission measurement and monitoring method, wherein the method comprises:

[0010] Collect equipment status information, energy composition information and working environment information during the operation of the target equipment to be measured for carbon emissions.

[0011] Analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past to obtain fused device status information, fused energy composition information and fused working environment information.

[0012] According to the fused device status information, fused energy composition information and fused working environment information, the metering accuracy information of the current carbon emission metering of the target device is obtained by classification.

[0013] During the operation of the target equipment, energy usage data is continuously monitored and acquired. According to the metering accuracy information, the energy usage data is input into the corresponding carbon emission metering channel in the carbon emission meter to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, an integrated metering quantity is set to perform carbon emission metering.

[0014] The present invention discloses a carbon emission metering and monitoring device and method, including: collecting device status information, energy composition information and working environment information generated by the target device during operation to measure carbon emissions. Analyze the floating ratio of the above information and standard information, set multiple time ranges, respectively integrate the historical information in each time range in the past, and generate the integrated device status information, energy composition information and working environment information. Based on the above fusion information, classify and obtain the metering accuracy information of the carbon emission metering of the target device. During the operation of the equipment, continuously monitor and obtain energy usage data. According to the metering accuracy information, input the energy usage data into the corresponding metering channel of the carbon emission meter. The carbon emission meter accurately measures carbon emissions according to multiple metering accuracy intervals, and integrates the data distributed in the corresponding metering accuracy intervals to finally obtain the carbon emission metering results. A carbon emission metering and monitoring device and method disclosed in the present invention solves the technical problems of insufficient monitoring accuracy, poor real-time performance and lack of traceability, and achieves the technical effect of improving monitoring accuracy and responsiveness and providing traceability monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural schematic diagram of a carbon emission measurement and monitoring device of the present invention;

[0016] Figure 2 The figure is a flow chart of a carbon emission measurement and monitoring method of the present invention.

[0017] Explanation of reference numerals: reference information acquisition module 11 , adaptive fusion module 12 , accuracy evaluation module 13 , emission metering module 14 . DETAILED DESCRIPTION

[0018] The technical solution provided in the embodiments of the present invention is to solve the technical problems of insufficient monitoring accuracy, poor real-time performance, and lack of traceability in the prior art. The overall idea adopted is as follows:

[0019] First, the device status information, energy composition information and working environment information of the target device to be carbon emission measured during operation are collected; then, the floating ratio between the device status information, energy composition information and working environment information and the standard information is analyzed, and multiple fusion time ranges are set to fuse the historical information within these time ranges to obtain fused device status information, fused energy composition information and fused working environment information; then, based on the fused device status information, energy composition information and working environment information, the metering accuracy information of the current carbon emission measurement of the target device is classified and calculated; then, during the operation of the target device, the energy usage data is continuously monitored, and according to the metering accuracy information, the energy usage data is input into the corresponding carbon emission metering channel of the carbon emission meter; then, carbon emission measurement is performed in the corresponding carbon emission metering channel, and the carbon emission measurement result is obtained; finally, according to the distribution in different metering accuracy intervals in the carbon emission meter, the integrated metering quantity is set to complete the accurate measurement of carbon emissions.

[0020] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0021] Embodiment 1:

[0022] Figure 1 The present invention is a structural schematic diagram of a carbon emission measurement and monitoring device, wherein the device comprises:

[0023] The reference information acquisition module 11 is used to collect the equipment status information, energy composition information and working environment information of the target equipment to be measured for carbon emissions during operation.

[0024] Specifically, the target carbon emission metering device is first interacted with to obtain the operating scenario information of the target device, including device status information, energy composition information and working environment information, so as to dynamically monitor the changes in the carbon emission environment, which will help to accurately measure the carbon emissions in the future.

[0025] Among them, equipment status information refers to the core operating parameters of the equipment during operation, such as working power, operating mode, load conditions, etc. This information will directly affect energy consumption and carbon emission levels. Energy composition information refers to the components of the energy consumed by the equipment, including the impurity content in the fuel, the type of fuel, the combustion quality, etc. These factors will affect the energy utilization efficiency and carbon emission level of the equipment. Working environment information refers to the relevant parameters in the equipment operating environment, such as ambient temperature, humidity, atmospheric pressure, etc. These environmental factors will affect the energy utilization efficiency of the equipment. For example, in a high temperature environment, the heat dissipation requirements of the equipment are higher, which will affect carbon emissions. In addition, the working environment information will also affect the heat dissipation efficiency of the carbon emission source, thereby affecting the amount of carbon emissions dissipated in the form of heat.

[0026] For example, if the ambient temperature is lower than the standard ambient temperature, the temperature difference between the hot section and the ambient temperature of the carbon emission source before the final emission is relatively large, that is, the heat transfer power (temperature gradient) for dissipating heat from the upstream of the carbon emission source to the ambient space is larger, and the corresponding heat dissipated is more than that under standard conditions. It is necessary to adjust the conversion coefficient between the calorific value obtained by the cold and hot carbon meter and the final output carbon emission measurement value accordingly (if the above-mentioned ambient temperature is lower than the standard ambient temperature, the conversion coefficient should be increased accordingly) to ensure the measurement accuracy of the cold and hot carbon meter.

[0027] In some embodiments, the device status information, energy composition information, and working environment information of the target device to be carbon emission measured are collected during operation, and the execution steps include:

[0028] Collect the equipment working power information of the target equipment to be measured for carbon emissions during operation as equipment status information. Perform sampling detection on the energy during the operation of the target equipment to obtain energy composition information. Collect the working environment information in the operating environment of the target equipment.

[0029] Specifically, working power information refers to the electrical power or fuel power consumed by the device during operation. It is a key parameter that affects energy efficiency and carbon emissions, and reflects the load status of the device. Different working powers correspond to different carbon emissions. For example, a power sensor or electric meter is installed on the target device to monitor the power consumption of the device in real time. The collected power data is combined with the device's operating time, load conditions, etc. to form a detailed power consumption curve, which reflects the operating status of the device as device status information.

[0030] Specifically, based on the energy type of the target device, the energy used by the target device is sampled and tested, where the energy composition information refers to the specific components of the energy consumed by the device. Such as the carbon content, impurity content, combustion efficiency, etc. in the fuel. Different fuel compositions lead to different carbon emissions. For example, if the device uses electricity, the power supply system is sampled to obtain indicators such as the type of power supply (thermal power, wind power, nuclear power, photovoltaic power, etc.), voltage fluctuations, harmonics, etc. as energy composition information; if gas or other fuels are used, samples are taken from the fuel supply system, and the energy composition information, including carbon content, proportion of other components, etc., is tested through analytical equipment or laboratories. By sampling and testing the energy composition consumed by the equipment, it is possible to accurately predict the carbon emissions of the equipment under specific energy use based on the quality of the fuel or the nature of the power supply.

[0031] Specifically, the working environment information includes the temperature, humidity, and air pressure of the environment where the equipment is located. By installing environmental sensors in the operating environment of the target equipment, the monitoring and collection of environmental parameters such as temperature, humidity, and air pressure can be achieved. Changes in the working environment will directly affect the efficiency of the equipment. For example, a high temperature environment may increase the energy consumption of the equipment, thereby affecting carbon emissions. Real-time collection of working environment information helps to accurately evaluate the impact of external environmental factors on equipment energy consumption and carbon emissions, thereby more accurately measuring carbon emissions.

[0032] The adaptive fusion module 12 is used to analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past to obtain fused device status information, fused energy composition information and fused working environment information.

[0033] Specifically, there is a floating ratio change between the data of equipment status information, energy composition information and working environment information and the standard information. The floating ratio refers to the degree of deviation between the real-time collected data and the preset standard value. A large floating ratio indicates that the data fluctuates greatly and there is a possibility of instability; while a small floating ratio indicates that the data is relatively stable.

[0034] Specifically, according to the size of the floating ratio, different fusion time ranges are set, and the data in the past time are fused and processed respectively to generate fusion device status information, fusion energy composition information and fusion working environment information. Among them, the fusion time range refers to the time window used to fuse data within a period of time. According to the size of the floating ratio, different fusion time ranges can be set to ensure the balance between accurate data analysis and processing efficiency.

[0035] In some embodiments, the floating ratio of the device status information, energy composition information, and working environment information to the standard information is analyzed, and multiple fusion time ranges are set. The execution steps include:

[0036] Obtain the standard device state information, standard energy composition information and standard working environment information of the target device. Calculate the state floating ratio, energy floating ratio and environment floating ratio based on the deviation between the device state information, energy composition information and working environment information and the standard device state information, standard energy composition information and standard working environment information. Adjust and calculate the preset fusion time range based on the state floating ratio, energy floating ratio and environment floating ratio to obtain the device fusion range, energy fusion range and environment fusion range, and set them as multiple fusion time ranges.

[0037] Specifically, standard equipment status information, standard energy composition information, and standard working environment information are ideal or expected data when the equipment is running. These standard values ​​come from the equipment manufacturer's specifications, industry standards, or historical data and are used as a comparison benchmark for current actual data.

[0038] Exemplarily, by accessing a standard information database storing standard data for various types of equipment, the model, type, configuration and other information of the target equipment are used as matching constraints to call the corresponding standard information set, including the equipment's operating power, energy efficiency, fuel composition, operating environment standards, etc.

[0039] Specifically, the floating ratio indicates the deviation between the current actual data and the standard information. The state floating ratio, energy floating ratio and environment floating ratio respectively indicate the degree of deviation of the equipment working state, energy composition and working environment from the standard value. Exemplarily, the state floating ratio is equal to the ratio of the difference between the actual equipment state information and the standard equipment state information to the standard equipment state information. The larger the floating ratio, the more the equipment operating state deviates from the standard. By calculating the state, energy and environment floating ratios, the degree of deviation between the actual equipment data and the standard information is quantified, which helps to analyze the stability and energy efficiency of equipment operation.

[0040] Specifically, the fusion time range is dynamically adjusted according to the floating ratio to ensure data stability and processing efficiency. When the floating ratio is large, it means that the current data has large fluctuations or instability. A longer fusion time range is selected accordingly, that is, the analysis time window of the data is lengthened, and data for a longer period of time is fused to reduce the impact of outliers and obtain steady-state data under the overall trend. When the floating ratio is small, the data is relatively stable, so a shorter fusion time range can be selected, that is, the data is fused in a short time, which reduces the complexity and computational cost of data processing and improves efficiency. By setting multiple fusion time ranges, flexible fusion processing can be performed on data with different floating ratios, thereby ensuring the accuracy and processing efficiency of data fusion.

[0041] Preferably, multiple different fusion time ranges are set according to different floating ratios. For example, the equipment status fusion time range may be 10 minutes, while the energy fusion time range may be 30 minutes to adapt to the fluctuation characteristics of different data types. Setting multiple fusion time ranges helps to adapt to different data types and fluctuation characteristics, flexibly adjust the fusion strategy, ensure that the smoothed data of equipment status, energy composition and working environment can reflect the actual situation, and provide reliable data support for subsequent carbon emission analysis.

[0042] The above steps analyze the floating ratio of equipment status, energy composition and working environment information to flexibly set the fusion time range to ensure the accuracy and stability of data fusion. In addition, multiple fusion time ranges are set to adopt appropriate fusion processing methods for data with different floating characteristics, thereby reducing the impact of abnormal data and improving data processing efficiency.

[0043] In some embodiments, historical information within the plurality of fusion time ranges in the past time is fused respectively to obtain fusion device status information, fusion energy component information and fusion working environment information, and the execution steps include:

[0044] Taking the current time node as the starting point, the historical device status information, historical energy composition information and historical working environment information within the multiple fusion time ranges in the past are retrieved respectively to obtain a set of historical device status information, a set of historical energy composition information and a set of historical working environment information. According to the set of historical device status information, the set of historical energy composition information and the set of historical working environment information, weighted calculations are performed to obtain weighted device status information, weighted energy composition information and weighted working environment information, wherein weight allocation and weighted calculations are performed according to the time difference with the current time node, and the size of the time difference is negatively correlated with the size of the weight. The weighted device status information, weighted energy composition information and weighted working environment information are combined with the device status information, energy composition information and working environment information to calculate and obtain fused device status information, fused energy composition information and fused working environment information.

[0045] Specifically, the historical information set of the device is retrieved forward based on the current time node, that is, the historical device status information, historical energy composition information and historical working environment information of the target device are extracted from the past time period for subsequent weighted calculation. Among them, the historical device status information set contains the working status data of the device at different time points, such as working power, load, operating mode, etc. The historical energy composition information set includes the composition data of the energy consumed by the device, such as the carbon content, impurity content or source of electricity of the fuel, etc. The historical working environment information set includes the environmental parameters when the device is running, such as ambient temperature, humidity, air pressure, etc.

[0046] Specifically, a weight is assigned according to the time difference between each data point and the current time. The closer the data is to the current time, the higher the weight is, and vice versa, the lower the weight is, to ensure that newer data has a greater impact on the results, thereby improving the timeliness of the data.

[0047] Exemplarily, the current device state information is fused with the weighted device state information, and the fused device state information = α×current device state information+(1-α)×weighted device state information fused device state information. Wherein, α is a weight allocation factor, and when the current data accounts for a large proportion, α is large.

[0048] Specifically, based on the same principle of the above-mentioned fusion weighted device status information, according to the fusion weighted energy composition information and weighted working environment information, it should be understood that for the sake of brevity of the specification, no further explanation is given here. The above-mentioned weighted calculation mechanism ensures that the data weight closer to the current is significant, effectively combines real-time and historical trends, so that the fusion information can better reflect the current operating status and future trends of the equipment.

[0049] The accuracy evaluation module 13 is used to classify and obtain the measurement accuracy information of the current carbon emission measurement of the target device according to the fused device status information, fused energy composition information and fused working environment information.

[0050] Specifically, by analyzing the fusion device status information, fusion energy composition information and fusion working environment information, the change of the unit energy carbon emission characteristics of the target device is obtained, thereby determining the accuracy of carbon emission measurement. Preferably, the measurement accuracy information for carbon emission measurement needs to meet the accuracy of at least one higher than the change of the unit energy carbon emission characteristics of the target device.

[0051] In some embodiments, according to the fused device status information, the fused energy composition information and the fused working environment information, the measurement accuracy information of the current carbon emission measurement of the target device is obtained by classification, and the execution steps include:

[0052] According to the historical carbon emission monitoring data of the target device, the sample fusion device status information set, the sample fusion energy component information set and the sample fusion working environment information set are collected, and the sample measurement accuracy information set is calculated and labeled according to the deviation between the carbon emissions and the standard carbon emissions. The sample fusion device status information set, the sample fusion energy component information set and the sample fusion working environment information set are used as input, and the sample measurement accuracy information set is used as output to train the measurement accuracy analyzer. The measurement accuracy analyzer is used to perform measurement accuracy analysis on the fusion device status information, fusion energy component information and fusion working environment information to obtain measurement accuracy information.

[0053] Specifically, first, the historical carbon emission data of the target device is obtained, including the device's operating power, energy composition and environmental information, as a sample fusion device state information set, a sample fusion energy composition information set and a sample fusion working environment information set. Among them, these data sets include carbon emissions under various operating states, different energy compositions and various environmental conditions, and the corresponding deviations from standard carbon emissions. Among them, the deviation from the standard carbon emissions refers to the reference carbon emissions in the carbon emission factor database under the target environment or target scenario.

[0054] Furthermore, based on the deviation between historical carbon emissions and standard carbon emissions, the measurement accuracy information of each sample is annotated, and the measurement accuracy label of each sample is generated to form a sample measurement accuracy information set for subsequent model training. Among them, when the deviation is large, the accuracy of the annotated carbon emission measurement is low, and vice versa, it indicates a high accuracy.

[0055] Specifically, the metering accuracy analyzer is a machine learning model trained based on historical sample data, which is used to predict the carbon emission metering accuracy of the equipment in the current state. Exemplarily, the metering accuracy analyzer is trained by taking the sample fusion equipment state information, the sample fusion energy composition information and the sample fusion working environment information set as the input features of the model, and taking the sample metering accuracy information set as the output label of the model for supervised learning to train the analyzer.

[0056] Specifically, the metering accuracy analyzer constructs feature vectors by extracting key features from device status, energy composition, and environmental information (such as changes in device power, fluctuations in energy composition, changes in ambient temperature, etc.), and models them through regression or classification models (such as linear regression, decision trees, neural networks, etc.). Then, the model parameters are repeatedly adjusted to improve the prediction accuracy of the metering accuracy analyzer. Finally, the performance of the model is evaluated through a validation set that was not involved in training to ensure that the metering accuracy analyzer performs well on unseen data.

[0057] Specifically, the trained metering accuracy analyzer combines the current operating status, energy composition and environmental information of the device to evaluate the current carbon emission metering accuracy in real time. For example, when the operating power of the device fluctuates greatly or the environment changes dramatically, the corresponding current carbon emission metering accuracy is low, and vice versa. By training the metering accuracy analyzer, the current carbon emission metering accuracy is evaluated in real time based on the device status, energy composition and working environment information, which helps to provide accurate metering feedback and determine the accuracy performance of the final metering results.

[0058] The emission metering module 14 is used to continuously monitor and obtain energy usage data during the operation of the target equipment, and input the energy usage data into the corresponding carbon emission metering channel in the carbon emission meter according to the metering accuracy information to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, sets the integrated metering quantity to perform carbon emission metering.

[0059] In some embodiments, according to the metering accuracy information, the energy usage data is input into a corresponding carbon emission metering channel in a carbon emission meter to perform carbon emission metering and obtain a carbon emission metering result. The execution steps include:

[0060] Train a carbon emission meter, wherein the carbon emission meter includes multiple carbon emission metering channels corresponding to multiple metering accuracy intervals, and each carbon emission metering channel includes multiple metering branches. Obtain the matching metering accuracy interval in which the metering accuracy information falls, and select the corresponding matching carbon emission metering channel. Calculate the deviation ratio between the metering accuracy information and the central metering accuracy information within the matching metering accuracy interval as the distribution position information of the metering accuracy information. According to the distribution position information, combined with the number of multiple metering branches in the matching carbon emission metering channel, calculate and obtain the integrated metering number. Input the energy usage data into the metering branch of the integrated metering number randomly selected in the matching carbon emission metering channel, obtain the branch carbon emission metering result of the integrated metering number through metering processing, and calculate the mean to obtain the carbon emission metering result of the target device.

[0061] Specifically, the carbon emission meter includes multiple carbon emission metering channels corresponding to multiple metering accuracy intervals, and each carbon emission metering channel is constructed based on integrated learning, including multiple metering branches; wherein, the multiple carbon emission metering channels are selected according to the metering accuracy interval in which the metering accuracy information is located, and the multiple metering branches within each carbon emission metering channel are selected according to the deviation between the current metering accuracy information and the central metering accuracy information within the metering accuracy interval.

[0062] Specifically, the metering accuracy information is matched with the preset metering accuracy interval to determine the matching carbon emission metering channel corresponding to the current carbon emission metering accuracy of the target device. Then, the difference between the metering accuracy information and the central metering accuracy information of the matching carbon emission metering channel is calculated, and the ratio of the difference to the central metering accuracy information is used as the distribution position information. Next, the distribution position information is multiplied by the number of multiple metering branches and rounded to obtain the integrated metering quantity. The greater the difference between the above metering accuracy information and the central metering accuracy information of the matching carbon emission metering channel, the greater the corresponding integrated metering quantity. In other words, the worse the distribution position information (the greater the above difference), it can be considered that the metering accuracy of the single metering branch of the currently selected carbon emission metering channel is less matched with the target device, and more metering branch integrated analysis is required to improve the accuracy. By determining the number of metering branches through the distribution position information, the integrated metering tasks can be reasonably allocated to ensure the accuracy and efficiency of carbon emission metering.

[0063] Furthermore, in the matching carbon emission measurement channel, metering branches of the integrated metering quantity are randomly selected, and the energy usage data is input into these branches for carbon emission measurement. Exemplarily, the energy usage data is metered and processed respectively through the selected metering branches of the integrated metering quantity to obtain the carbon emission measurement results of the branches of the integrated metering quantity, and then the carbon emission measurement results of all branches are averaged to obtain the carbon emission measurement results of the target device. Through the above-mentioned multiple metering branches, data is processed in parallel, which helps to reduce the impact of single branch errors on the final results and ensure the stability and accuracy of carbon emission measurement.

[0064] According to the sample measurement accuracy information set, a plurality of measurement accuracy intervals are obtained by division.

[0065] Based on the historical operation and carbon emission measurement data of the target device, sample energy usage data sets and sample carbon emission measurement result sets under the multiple metering accuracy intervals are collected respectively to obtain multiple metering training data sets. In the first metering training data set corresponding to the first metering accuracy interval, multiple first metering training data are randomly selected, and multiple metering branches are trained respectively to obtain multiple metering branches that have been trained, and the first carbon emission metering channel is obtained by combination. Continue to use other multiple channels, train to obtain multiple carbon emission metering channels, and obtain the carbon emission meter.

[0066] Specifically, first, according to the sample metering accuracy information set, analyze the deviation between the carbon emissions and the standard carbon emissions. Divide the samples into multiple intervals according to the metering accuracy (deviation). For example, divide the deviation into different intervals such as ±1%, ±2%, and ±3%. Then, based on the multiple metering accuracy intervals obtained by the division, mark the metering accuracy interval to which each sample belongs, and according to the marking results, divide the historical operation and carbon emission measurement data of the target equipment into multiple metering training data sets, wherein each metering training data set includes a sample energy usage data set and a sample carbon emission metering result set within a specified interval, and the sample energy usage data set and the sample carbon emission metering result set are stored in a one-to-one correspondence.

[0067] Specifically, each metering accuracy interval in the multiple metering accuracy intervals corresponds to a training data set, which includes energy usage data and carbon emission metering results in the accuracy interval, and is used to train different metering models.

[0068] Specifically, to train the carbon emission metering channel, first, in the first metering training data set, randomly select multiple sample data as multiple first metering training data to ensure the diversity and representativeness of the training data. Then, construct multiple metering branches, input the selected multiple first metering training data into different multiple metering branches for training, and each branch is trained independently to improve the metering accuracy in different scenarios. Finally, after the training is completed, all metering branches are combined to form the first carbon emission metering channel corresponding to the first accuracy interval.

[0069] The multiple measurement branches refer to multiple parallel measurement models or algorithms trained in each measurement accuracy interval for processing carbon emission measurement. The multiple measurement branches may adopt different measurement models or algorithms with different complexities. For example, the multiple measurement branches may be constructed based on a linear regression model, a decision tree model or a neural network model.

[0070] Furthermore, the above training process is repeated to continue extracting sample data of other precision intervals and training multiple measurement branches. The training data set and measurement branch corresponding to each precision interval are used as independent training data to ensure that each precision interval can obtain an exclusive carbon emission measurement channel. Each carbon emission measurement channel corresponds to the carbon emission measurement capability under different precision intervals. By training multiple channels, it is helpful to flexibly respond to carbon emission measurement needs in different scenarios.

[0071] Specifically, after training is completed within all metering accuracy ranges, all carbon emission metering channels are combined together to form a complete carbon emission meter. The generated carbon emission meter can select appropriate metering channels for processing according to the metering accuracy requirements of the actual equipment to ensure the accuracy and reliability of the carbon emission metering results.

[0072] In summary, the carbon emission measurement and monitoring device provided by the present invention has the following technical effects:

[0073] Collect the equipment status information, energy composition information and working environment information generated by the target equipment during operation to measure carbon emissions. Analyze the floating ratio of the above information to the standard information, set multiple time ranges, and fuse the historical information in each time range in the past to generate the fused equipment status information, energy composition information and working environment information. Based on the above fusion information, classify and obtain the measurement accuracy information of the carbon emission measurement of the target equipment. During the operation of the equipment, continuously monitor and obtain energy usage data. According to the measurement accuracy information, input the energy usage data into the corresponding measurement channel of the carbon emission meter. The carbon emission meter accurately measures carbon emissions according to multiple measurement accuracy intervals, and integrates the data distributed in the corresponding measurement accuracy intervals to finally obtain the carbon emission measurement results. This can improve the monitoring accuracy and responsiveness and provide the technical effect of traceability monitoring.

[0074] Embodiment 2:

[0075] Figure 2 Schematic diagram of a carbon emission measurement and monitoring method of the present invention. For example, Figure 1 The structural schematic diagram of a carbon emission measurement and monitoring device of the present invention is used to achieve the following Figure 2 The method shown.

[0076] Based on the same concept as the carbon emission measurement and monitoring device in the embodiment, the present invention also provides a carbon emission measurement and monitoring method including:

[0077] Collect equipment status information, energy composition information and working environment information during the operation of the target equipment to be measured for carbon emissions.

[0078] Analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past to obtain fused device status information, fused energy composition information and fused working environment information.

[0079] According to the fused device status information, fused energy composition information and fused working environment information, the metering accuracy information of the current carbon emission metering of the target device is obtained by classification.

[0080] During the operation of the target equipment, energy usage data is continuously monitored and acquired. According to the metering accuracy information, the energy usage data is input into the corresponding carbon emission metering channel in the carbon emission meter to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, an integrated metering quantity is set to perform carbon emission metering.

[0081] In some embodiments, the device status information, energy composition information, and working environment information of the target device to be carbon emission measured during operation are collected, including:

[0082] The equipment working power information of the target equipment to be carbon emission measured during operation is collected as equipment status information.

[0083] The energy during the operation of the target device is sampled and tested to obtain energy composition information.

[0084] Collect working environment information in the operating environment of the target device.

[0085] In some embodiments, the floating ratio of the device status information, energy composition information, and working environment information to the standard information is analyzed, and multiple fusion time ranges are set, including:

[0086] Acquire standard device status information, standard energy composition information, and standard working environment information of the target device.

[0087] According to the deviations of the device state information, energy composition information and working environment information from the standard device state information, standard energy composition information and standard working environment information, the state floating ratio, energy floating ratio and environment floating ratio are calculated.

[0088] According to the state floating ratio, energy floating ratio and environment floating ratio, the preset fusion time range is adjusted and calculated to obtain the equipment fusion range, energy fusion range and environment fusion range, which are set as multiple fusion time ranges.

[0089] In some embodiments, the historical information within the multiple fusion time ranges in the past time is fused respectively to obtain fusion device status information, fusion energy component information and fusion working environment information, including:

[0090] Taking the current time node as the starting point, the historical equipment status information, historical energy composition information and historical working environment information within the multiple fusion time ranges in the past are retrieved respectively to obtain a historical equipment status information set, a historical energy composition information set and a historical working environment information set.

[0091] According to the historical device status information set, the historical energy composition information set and the historical working environment information set, weighted calculations are performed respectively to obtain weighted device status information, weighted energy composition information and weighted working environment information, wherein weight allocation and weighted calculation are performed according to the time difference with the current time node, and the size of the time difference is negatively correlated with the size of the weight.

[0092] The weighted device status information, weighted energy composition information and weighted working environment information are combined with the device status information, energy composition information and working environment information to calculate and obtain fused device status information, fused energy composition information and fused working environment information.

[0093] In some embodiments, according to the fused device status information, the fused energy composition information and the fused working environment information, the measurement accuracy information of the current carbon emission measurement of the target device is obtained by classification, including:

[0094] According to the historical carbon emission monitoring data of the target equipment, the sample fusion equipment status information set, the sample fusion energy composition information set and the sample fusion working environment information set are collected, and the sample measurement accuracy information set is calculated and labeled based on the deviation between the carbon emissions and the standard carbon emissions.

[0095] The sample fusion device state information set, the sample fusion energy component information set and the sample fusion working environment information set are used as input, and the sample measurement accuracy information set is used as output to train a measurement accuracy analyzer.

[0096] The metering accuracy analyzer is used to perform metering accuracy analysis on the fusion device status information, fusion energy composition information and fusion working environment information to obtain metering accuracy information.

[0097] In some embodiments, according to the metering accuracy information, the energy usage data is input into a corresponding carbon emission metering channel in a carbon emission meter to perform carbon emission metering to obtain a carbon emission metering result, including:

[0098] A carbon emission meter is trained, wherein the carbon emission meter includes a plurality of carbon emission metering channels corresponding to a plurality of metering accuracy intervals, and each carbon emission metering channel includes a plurality of metering branches.

[0099] The matching metering accuracy interval that the metering accuracy information falls into is obtained, and a corresponding matching carbon emission metering channel is selected.

[0100] The deviation ratio between the metering accuracy information and the central metering accuracy information within the matching metering accuracy interval is calculated as the distribution position information of the metering accuracy information.

[0101] According to the distribution position information, combined with the number of multiple metering branches in the matching carbon emission metering channel, the integrated metering quantity is calculated and obtained.

[0102] The energy usage data is input into the metering branch of the randomly selected integrated metering quantity in the matching carbon emission metering channel, and the metering process is performed to obtain the carbon emission metering result of the branch of the integrated metering quantity, and the mean is calculated to obtain the carbon emission metering result of the target device.

[0103] In some implementations, training a carbon emissions meter includes:

[0104] According to the sample measurement accuracy information set, a plurality of measurement accuracy intervals are obtained by division.

[0105] Based on the historical operation and carbon emission measurement data of the target device, sample energy usage data sets and sample carbon emission measurement result sets under the multiple metering accuracy intervals are collected respectively to obtain multiple metering training data sets.

[0106] In the first metering training data set corresponding to the first metering accuracy interval, multiple copies of the first metering training data are randomly selected, and multiple metering branches are trained respectively to obtain multiple metering branches that have completed the training, and the first carbon emission metering channel is obtained by combining them.

[0107] Continue to use other multiple training to obtain multiple carbon emission metering channels and obtain the carbon emission meter.

[0108] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to a carbon emission measurement and monitoring method described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0109] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A carbon emission measurement and monitoring device, characterized in that: The device comprises: A reference information acquisition module, which is used to collect equipment status information, energy composition information and working environment information during the operation of the target equipment to be measured for carbon emissions; An adaptive fusion module, the adaptive fusion module is used to analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past time to obtain fused device status information, fused energy composition information and fused working environment information; An accuracy evaluation module, wherein the accuracy evaluation module is used to classify and obtain the current measurement accuracy information of carbon emission measurement for the target device according to the fusion device status information, the fusion energy composition information and the fusion working environment information; An emission metering module, wherein the emission metering module is used to continuously monitor and obtain energy usage data during the operation of the target equipment, and input the energy usage data into the corresponding carbon emission metering channel in the carbon emission meter according to the metering accuracy information, to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, an integrated metering quantity is set to perform carbon emission metering.

2. The carbon emission measurement and monitoring device according to claim 1 is characterized in that: Collect the equipment status information, energy composition information and working environment information of the target equipment to be measured for carbon emissions during operation. The execution steps include: Collecting equipment operating power information during the operation of the target equipment to be carbon emission measured as equipment status information; Sampling and testing the energy during the operation of the target device to obtain energy composition information; Collect working environment information in the operating environment of the target device.

3. The carbon emission measurement and monitoring device according to claim 1 is characterized in that: Analyze the floating ratio of the equipment status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and execute the steps including: Acquiring standard device status information, standard energy composition information, and standard working environment information of the target device; Calculate the state floating ratio, energy floating ratio and environment floating ratio according to the deviations between the device state information, energy composition information and working environment information and the standard device state information, standard energy composition information and standard working environment information; According to the state floating ratio, energy floating ratio and environment floating ratio, the preset fusion time range is adjusted and calculated to obtain the equipment fusion range, energy fusion range and environment fusion range, which are set as multiple fusion time ranges.

4. The carbon emission measurement and monitoring device according to claim 1, characterized in that: The historical information within the multiple fusion time ranges in the past time are respectively fused to obtain fusion device status information, fusion energy component information and fusion working environment information. The execution steps include: Taking the current time node as the starting point, respectively searching for the historical equipment status information, the historical energy composition information and the historical working environment information within the multiple fusion time ranges in the past time, to obtain a historical equipment status information set, a historical energy composition information set and a historical working environment information set; According to the historical device status information set, the historical energy composition information set and the historical working environment information set, respectively weighted calculations are performed to obtain weighted device status information, weighted energy composition information and weighted working environment information, wherein weight allocation and weighted calculation are performed according to the time difference with the current time node, and the size of the time difference is negatively correlated with the size of the weight; The weighted device status information, weighted energy composition information and weighted working environment information are combined with the device status information, energy composition information and working environment information to calculate and obtain fused device status information, fused energy composition information and fused working environment information.

5. The carbon emission measurement and monitoring device according to claim 1, characterized in that: According to the fused device status information, fused energy composition information and fused working environment information, the measurement accuracy information of the current carbon emission measurement of the target device is obtained by classification, and the execution steps include: According to the historical carbon emission monitoring data of the target equipment, the sample fusion equipment status information set, the sample fusion energy composition information set and the sample fusion working environment information set are collected, and according to the deviation between the carbon emissions and the standard carbon emissions, the sample measurement accuracy information set is calculated and annotated; The sample fusion device state information set, the sample fusion energy component information set and the sample fusion working environment information set are used as input, and the sample measurement accuracy information set is used as output to train a measurement accuracy analyzer; The metering accuracy analyzer is used to perform metering accuracy analysis on the fusion device status information, fusion energy composition information and fusion working environment information to obtain metering accuracy information.

6. The carbon emission measurement and monitoring device according to claim 5, characterized in that: According to the metering accuracy information, the energy usage data is input into the corresponding carbon emission metering channel in the carbon emission meter to perform carbon emission metering and obtain the carbon emission metering result. The execution steps include: Training a carbon emission meter, wherein the carbon emission meter includes a plurality of carbon emission metering channels corresponding to a plurality of metering precision intervals, and each carbon emission metering channel includes a plurality of metering branches; Obtaining the matching measurement accuracy interval that the measurement accuracy information falls into, and selecting the corresponding matching carbon emission measurement channel; Calculating a deviation ratio between the metering accuracy information and the central metering accuracy information within the matching metering accuracy interval as distribution position information of the metering accuracy information; According to the distribution location information, combined with the number of multiple metering branches in the matching carbon emission metering channel, an integrated metering quantity is calculated; The energy usage data is input into a metering branch of an integrated metering quantity randomly selected in the matching carbon emission metering channel, and the metering processing is performed to obtain the branch carbon emission metering results of the integrated metering quantity, and the mean is calculated to obtain the carbon emission metering result of the target device.

7. The carbon emission measurement and monitoring device according to claim 6, characterized in that: To train the carbon emission meter, the following steps are performed: According to the sample measurement accuracy information set, a plurality of measurement accuracy intervals are obtained by division; Based on the historical operation and carbon emission measurement data of the target device, respectively collect a sample energy usage data set and a sample carbon emission measurement result set under the multiple metering accuracy intervals to obtain multiple metering training data sets; In the first metering training data set corresponding to the first metering accuracy interval, a plurality of first metering training data are randomly selected, and a plurality of metering branches are trained respectively to obtain a plurality of metering branches that have been trained, and the first carbon emission metering channel is obtained by combining them; Continue to use other multiple training to obtain multiple carbon emission metering channels and obtain the carbon emission meter.

8. A carbon emission measurement and monitoring method, characterized in that: The method is applied to a carbon emission measurement and monitoring device according to any one of claims 1 to 7, and the method comprises: Collect equipment status information, energy composition information and working environment information during the operation of the target equipment to be measured for carbon emissions; Analyze the floating ratio of the device status information, energy composition information and working environment information to the standard information, set multiple fusion time ranges, and fuse the historical information within the multiple fusion time ranges in the past to obtain fused device status information, fused energy composition information and fused working environment information; According to the fused device status information, fused energy composition information and fused working environment information, classify and obtain the current measurement accuracy information of carbon emission measurement for the target device; During the operation of the target equipment, energy usage data is continuously monitored and acquired. According to the metering accuracy information, the energy usage data is input into the corresponding carbon emission metering channel in the carbon emission meter to perform carbon emission metering and obtain carbon emission metering results, wherein the carbon emission meter includes carbon emission metering channels corresponding to multiple metering accuracy intervals, and according to the distribution of the metering accuracy information in the corresponding metering accuracy interval, an integrated metering quantity is set to perform carbon emission metering.

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