A carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data.

Abstract

This invention discloses a carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data, belonging to the field of carbon emission analysis technology. It includes a dual-carbon data center, a prediction model creation module, a carbon emission control system, a data monitoring module, a data acquisition module, a data verification module, a processing and analysis module, and a control and management module. This carbon emission anomaly monitoring and analysis system acquires dual-carbon data from enterprises within the carbon emission control system through the data acquisition module, sends it to the data verification module to determine if there are any anomalies in the carbon emission data, and then sends the abnormal data to the processing and analysis module to derive the inherent patterns and trends of the abnormal data. Based on the analysis results, corresponding control commands are sent. The improved carbon emission anomaly monitoring and analysis system can organize and classify abnormal data, combine analysis to derive the inherent patterns and trends of the abnormal data, and send control commands to promote the recovery of enterprises with abnormal carbon emissions to normal levels.

Description

Technical Field

[0001] This invention relates to the field of carbon emission analysis technology, specifically to a carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data. Background Technology

[0002] During the production process, enterprises typically consume energy, which in turn generates carbon emissions. Since carbon emissions have a significant impact on the environment, it is necessary to monitor the carbon emission data of enterprises for environmental protection purposes, such as through regional monitoring and management. However, due to the large volume of data, which far exceeds the capacity for manual processing, current technology generally uses carbon emission anomaly monitoring and analysis systems to replace manual labor in monitoring enterprise carbon emissions.

[0003] Existing monitoring and analysis systems can automatically monitor corporate carbon emissions and issue warnings when carbon emission data is abnormal. However, the criteria for judging whether something is abnormal are usually set manually based on experience. Due to differences in corporate size and industry, there are differences in carbon emission data, resulting in differences in energy consumption and carbon emissions, which affects the accuracy of the system's anomaly detection. In addition, since the abnormal data itself also varies, the resulting carbon emission anomalies also vary. Existing monitoring and analysis systems cannot make targeted adjustments based on the differences in anomalies, which is not conducive to corporate carbon emissions. Summary of the Invention

[0004] In view of this, the present invention provides a carbon emission anomaly monitoring and analysis system that can sort and classify abnormal data, which can accurately identify whether there are anomalies in the carbon emission data of enterprises in real time and analyze the abnormal data, so as to play a role in timely supervision and targeted regulation.

[0005] The technical solution adopted by the embodiments of the present invention to solve its technical problem is as follows:

[0006] A carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data, characterized in that it includes:

[0007] The dual-carbon data center is used to store the original dual-carbon data of all enterprises within the carbon emission control system; and it can receive query requests, retrieve dual-carbon data according to the query requests, and send them to the prediction model creation module.

[0008] The prediction model creation module is used to predict carbon data based on the original enterprise carbon data, obtain the predicted data, and output it to the data verification module; the prediction model creation module is specifically used to perform the following steps:

[0009] The original enterprise dual-carbon data is obtained from the dual-carbon data center. Training data and prediction data are set based on the original enterprise dual-carbon data. The training sample data is normalized, a BP neural network is constructed and the network parameters are configured. The BP neural network is trained, and the network structure is adjusted according to the training results to form a BP network model.

[0010] The carbon emission control system includes, but is not limited to, all controlled enterprises; it is used to provide real-time enterprise dual carbon data.

[0011] The data monitoring module is used to monitor whether the data retrieval, transmission and updating within the dual-carbon data center and the carbon emission control system are normal.

[0012] The data acquisition module collects real-time enterprise dual-carbon data from the carbon emission control system through the device and inputs it into the data verification module. The collected real-time enterprise dual-carbon data includes, but is not limited to, enterprise name, energy consumption data, carbon emission data, percentage data, and emission time period data.

[0013] The data verification module is used to compare the difference between the real-time enterprise dual carbon data and the predicted data to verify whether there is any anomaly in the real-time enterprise dual carbon data, and to define the real-time enterprise dual carbon data with anomalies as abnormal data.

[0014] The processing and analysis module is used to organize and classify the abnormal data detected by the data inspection module, and to analyze the inherent patterns of the abnormal data and obtain the abnormal data analysis results.

[0015] The regulation and management module is used to send regulation instructions to the enterprises corresponding to the abnormal data within the carbon emission control system based on the abnormal data analysis results, so that the enterprises corresponding to the abnormal data can make corresponding carbon emission adjustment and control according to the regulation instructions and promote the data to return to normal.

[0016] Preferably, the first output terminal of the data monitoring module is connected to the dual-carbon data center, and the second output terminal of the data monitoring module is connected to the carbon emission control system. The dual-carbon data center and the carbon emission control system operate independently and do not interfere with each other.

[0017] Preferably, the input end of the dual-carbon data center is connected to the output end of the data verification module, and the data stored in the dual-carbon data center includes normal data, abnormal data, raw data, and operation and maintenance data.

[0018] Preferably, the data verification module includes a data comparison unit, a difference analysis unit, and an anomaly detection unit, wherein the output of the data comparison unit is connected to the input of the difference analysis unit, and the output of the difference analysis unit is connected to the input of the anomaly detection unit.

[0019] Preferably, the first input terminal of the data comparison unit is connected to the BP network model to receive the prediction data transmitted by the BP network model, and the second input terminal of the data comparison unit is connected to the data acquisition module to receive the data transmitted by the data acquisition module in real time.

[0020] Preferably, the data comparison unit, the difference analysis unit, and the anomaly detection unit are sequential; the data comparison unit compares the predicted data and the real-time enterprise dual-carbon data to obtain data differences and sends them to the difference analysis unit; the difference analysis unit analyzes the data differences to obtain the magnitude of the data differences; and then the anomaly detection unit determines whether the data differences are within the normal range based on the magnitude of the data differences.

[0021] Preferably, the discrimination formula of the anomaly discrimination unit is as follows:

[0022]

[0023] Wherein, CV is the coefficient of difference, X_pd is the predicted data, X_rd is the carbon emission data, i represents any integer from 1 to n, n represents the total number of enterprises, k is the normal range value, S is the abnormal data discrimination result, and s1 and s2 represent discrimination result 1 and discrimination result 2, respectively.

[0024] Preferably, there are only two types of anomaly detection results: detection result s1 and detection result s2. Detection result s1 indicates that the data difference is within the normal range, the real-time enterprise dual carbon data is not abnormal, and is output as normal data. Detection result s2 indicates that the data difference exceeds the normal range, the real-time enterprise dual carbon data is abnormal, and is output as abnormal data.

[0025] Preferably, the processing and analysis module includes a data processing unit, a data classification unit, and an anomaly analysis unit. The data processing unit is used to receive and process the abnormal data, the data classification unit is used to classify data according to data characteristics, and the anomaly analysis unit is used to analyze the abnormal data. The anomaly analysis unit performs comparative analysis and trend analysis in sequence.

[0026] Preferably, the usage process of the carbon emission anomaly monitoring and analysis system that can organize and classify abnormal data is as follows:

[0027] Step 1: The prediction model creation module sends a request to the dual-carbon data center to obtain the original enterprise dual-carbon data, and establishes a BP network model based on the original enterprise dual-carbon data.

[0028] Step 2: The data acquisition module acquires real-time enterprise dual-carbon data within the carbon emission control system and sends it to the data verification module. After receiving the real-time enterprise dual-carbon data and the predicted data transmitted by the BP network model, the data comparison unit compares the data to determine the data difference and sends it to the difference analysis unit. The difference analysis unit analyzes the data difference to determine its magnitude. Then, the difference discrimination unit determines whether the data difference is within the normal range based on the magnitude of the data difference, thereby determining whether the real-time enterprise dual-carbon data is abnormal.

[0029] Step 3: The abnormal data is sent to the processing and analysis module. The data processing unit receives and processes the abnormal data. Then, the data classification module divides the data into categories according to its characteristics. Next, the anomaly analysis unit executes comparative analysis and trend analysis commands in sequence to analyze and obtain the abnormal data analysis results. The abnormal data analysis results include the inherent patterns and trends of the abnormal data.

[0030] Step 4: The control and management module sends control instructions to the enterprises corresponding to the abnormal data within the carbon emission control system based on the abnormal data analysis results, so that the enterprises corresponding to the abnormal data can make corresponding carbon emission adjustment and control according to the control instructions and promote the data to return to normal.

[0031] As can be seen from the above technical solution, the carbon emission anomaly monitoring and analysis system provided by the embodiments of the present invention, which can classify and organize abnormal data, has the following beneficial effects: The present invention obtains data from the dual-carbon data center through the prediction model creation module, establishes a BP network model, and obtains dual-carbon data of enterprises within the carbon emission control system through the data acquisition module. The data is then sent to the data verification module to determine whether there are any anomalies in the carbon emission data. The abnormal data is then sent to the processing and analysis module to obtain the inherent laws and trends of the abnormal data. Finally, the control and management module sends control instructions to enterprises within the carbon emission control system that have data anomalies based on the abnormal data analysis results, thereby realizing carbon emission control. The improved carbon emission anomaly monitoring and analysis system can not only accurately determine whether the carbon emission data of enterprises is abnormal by using models and formulas in combination, but also classify and organize the abnormal data, and combine analysis to obtain the inherent laws and trends of the abnormal data, thereby sending control instructions to promote the recovery of enterprises with abnormal carbon emissions to normal. Attached Figure Description

[0032] Figure 1This is a schematic diagram of the overall operation process of a carbon emission anomaly monitoring and analysis system that can sort and classify abnormal data according to the present invention.

[0033] Figure 2 This is a schematic diagram of a dual-carbon data center-carbon emission control system for a carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data, according to the present invention.

[0034] Figure 3 This is a schematic diagram of the operation process of the data verification module of a carbon emission anomaly monitoring and analysis system that can sort and classify abnormal data according to the present invention.

[0035] Figure 4 This is a schematic diagram of the operation flow of the processing and analysis module of a carbon emission anomaly monitoring and analysis system that can sort and classify abnormal data according to the present invention.

[0036] Figure 5 This is a schematic diagram of the BP network model creation process for a carbon emission anomaly monitoring and analysis system that can organize and classify abnormal data, according to the present invention. Detailed Implementation

[0037] The technical solution and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] like Figures 1-5 As shown, a carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data includes:

[0039] The dual-carbon data center is used to store the original dual-carbon data of all enterprises within the carbon emission control system; and can receive query requests, retrieve dual-carbon data according to the query requests, and send them to the prediction model creation module; the original enterprise dual-carbon data includes, but is not limited to, enterprise name, energy consumption data, carbon emission data, percentage data, and emission time period data.

[0040] The prediction model creation module is used to predict carbon data based on the original enterprise carbon data, obtain the predicted data, and output it to the data verification module; the prediction model creation module is specifically used to perform the following steps:

[0041] The original enterprise dual-carbon data is obtained from the dual-carbon data center. Training data and prediction data are set based on the original enterprise dual-carbon data. The training sample data is normalized, a BP neural network is constructed and the network parameters are configured. The BP neural network is trained, and the network structure is adjusted according to the training results to form a BP network model.

[0042] The carbon emission control system includes, but is not limited to, all controlled enterprises; it is used to provide real-time enterprise dual carbon data.

[0043] The data monitoring module is used to monitor whether the data retrieval, transmission and updating within the dual-carbon data center and the carbon emission control system are normal.

[0044] The data acquisition module collects real-time enterprise dual-carbon data from the carbon emission control system via equipment and inputs it into the data verification module. The collected real-time enterprise dual-carbon data includes, but is not limited to, enterprise name, energy consumption data, carbon emission data, percentage data, and emission time period data. The first output terminal of the data monitoring module is connected to the dual-carbon data center, and the second output terminal of the data monitoring module is connected to the carbon emission control system. The dual-carbon data center and the carbon emission control system operate independently and do not interfere with each other.

[0045] The data verification module is used to compare the difference between the real-time enterprise dual-carbon data and the predicted data to verify whether there are any anomalies in the real-time enterprise dual-carbon data, and to define the real-time enterprise dual-carbon data with anomalies as abnormal data; the input end of the dual-carbon data center is connected to the output end of the data verification module, and the data stored in the dual-carbon data center includes normal data, abnormal data, raw data and operation and maintenance data;

[0046] The data verification module includes a data comparison unit, a difference analysis unit, and an anomaly detection unit. The output of the data comparison unit is connected to the input of the difference analysis unit, and the output of the difference analysis unit is connected to the input of the anomaly detection unit. The first input of the data comparison unit is connected to the BP network model to receive the predicted data transmitted by the BP network model, and the second input of the data comparison unit is connected to the data acquisition module to receive data transmitted in real time by the data acquisition module. Here, the data transmitted in real time by the data acquisition module refers to real-time enterprise carbon data.

[0047] The data comparison unit, the difference analysis unit, and the anomaly detection unit are sequential; the data comparison unit compares the predicted data and the real-time enterprise dual-carbon data to obtain data differences and sends them to the difference analysis unit; the difference analysis unit analyzes the data differences to obtain the magnitude of the data differences; then the anomaly detection unit determines whether the data differences are within the normal range based on the magnitude of the data differences.

[0048] The discrimination formula of the anomaly discrimination unit is as follows:

[0049]

[0050] Wherein, CV is the coefficient of difference, X_pd is the predicted data, X_rd is the real-time corporate carbon emission data, i represents any integer from 1 to n, n represents the total number of enterprises, k is the normal range value, S is the abnormal data discrimination result, and s1 and s2 represent discrimination result 1 and discrimination result 2, respectively.

[0051] There are only two types of anomaly detection results: detection result s1 and detection result s2. Detection result s1 indicates that the data difference is within the normal range, the real-time enterprise dual carbon data is not abnormal, and is output as normal data. Detection result s2 indicates that the data difference exceeds the normal range, the real-time enterprise dual carbon data is abnormal, and is output as abnormal data.

[0052] The processing and analysis module includes a data processing unit, a data classification unit, and an anomaly analysis unit. The data processing unit is used to receive and process the abnormal data, the data classification unit is used to classify data according to its characteristics, and the anomaly analysis unit is used to analyze the abnormal data. The anomaly analysis unit performs comparative analysis and trend analysis in sequence.

[0053] The processing and analysis module is used to organize and classify the abnormal data detected by the data inspection module, and to analyze the inherent patterns of the abnormal data and obtain the abnormal data analysis results.

[0054] The regulation and management module is used to send regulation instructions to the enterprises corresponding to the abnormal data within the carbon emission control system based on the abnormal data analysis results, so that the enterprises corresponding to the abnormal data can make corresponding carbon emission adjustment and control according to the regulation instructions and promote their data to return to normal.

[0055] The aforementioned dual-carbon data for enterprises includes, but is not limited to, enterprise name, energy consumption data, carbon emission data, percentage data, and emission time period data. Therefore, the data involved in a single CV calculation can be a collection of all types of data for an enterprise, or a collection of one or several types of data (i.e., overall analysis, partial combination analysis, or individual analysis). Thus, the range of data identified as anomalous data after a single CV result analysis can be all data in the real-time enterprise carbon emission data, or only some data in the real-time enterprise carbon emission data. Therefore, the data types of anomalous data stored in the dual-carbon data center may differ.

[0056] In summary, combining Figures 1-5 As shown, the carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data is characterized by the following usage process:

[0057] Step 1: The prediction model creation module sends a request to the dual-carbon data center to obtain the enterprise's original dual-carbon data and builds a BP network model based on the dual-carbon data.

[0058] Step 2: The data acquisition module acquires the dual carbon data of enterprises within the carbon emission control system and sends it to the data verification module. After receiving the predicted data transmitted by the BP network model and the real-time data transmitted by the data acquisition module, the data comparison unit compares the data to find the data differences and sends them to the difference analysis unit to analyze the magnitude of the data differences. Then, the difference analysis unit uses a formula to calculate and determine whether there are any abnormalities in the data.

[0059] Step 3: Abnormal data is sent to the processing and analysis module. The data processing unit receives and processes the abnormal data. Then, the data classification module divides the data into categories according to its characteristics. Next, the abnormal analysis unit executes the comparative analysis and trend analysis commands in sequence to analyze and obtain the inherent patterns and trends of the abnormal data.

[0060] Step 4: The control and management module sends control instructions to enterprises with abnormal data within the carbon emission control system based on the abnormal data analysis results. After receiving the control instructions, the enterprises make adjustments and controls accordingly.

[0061] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A carbon emission anomaly monitoring and analysis system capable of organizing and classifying abnormal data, characterized in that, include: The dual-carbon data center is used to store the original dual-carbon data of all enterprises within the carbon emission control system. It can also receive query requests, retrieve dual-carbon data according to the query requests, and send them to the prediction model creation module; The prediction model creation module is used to predict carbon data based on the original enterprise carbon data, obtain the predicted data, and output it to the data verification module; the prediction model creation module is specifically used to perform the following steps: The original enterprise dual-carbon data is obtained from the dual-carbon data center. Training data and prediction data are set based on the original enterprise dual-carbon data. The training sample data is normalized, a BP neural network is constructed and the network parameters are configured. The BP neural network is trained, and the network structure is adjusted according to the training results to form a BP network model. The carbon emission control system includes all enterprises under control; Used to provide real-time enterprise dual carbon data; The data monitoring module is used to monitor whether the data retrieval, transmission and updating within the dual-carbon data center and the carbon emission control system are normal. The data acquisition module collects real-time enterprise dual-carbon data from the carbon emission control system through the device and inputs it into the data verification module. The collected real-time enterprise dual-carbon data includes enterprise name, energy consumption data, carbon emission data, percentage data, and emission time period data. The data verification module is used to compare the difference between the real-time enterprise dual carbon data and the predicted data to verify whether there is any anomaly in the real-time enterprise dual carbon data, and to define the real-time enterprise dual carbon data with anomalies as abnormal data. The processing and analysis module is used to organize and classify the abnormal data detected by the data inspection module, and to analyze the inherent patterns of the abnormal data and obtain the abnormal data analysis results. The regulation and management module is used to send regulation instructions to the enterprises corresponding to the abnormal data within the carbon emission control system based on the abnormal data analysis results, so that the enterprises corresponding to the abnormal data can make corresponding carbon emission adjustment and control according to the regulation instructions and promote the data to return to normal. The data verification module includes a data comparison unit, a difference analysis unit, and an anomaly detection unit. The output of the data comparison unit is connected to the input of the difference analysis unit, and the output of the difference analysis unit is connected to the input of the anomaly detection unit. The data comparison unit, the difference analysis unit, and the anomaly detection unit are sequential; the data comparison unit compares the predicted data and the real-time enterprise dual-carbon data to obtain data differences and sends them to the difference analysis unit; the difference analysis unit analyzes the data differences to determine the magnitude of the data differences; then the anomaly detection unit determines whether the data differences are within the normal range based on the magnitude of the data differences. The discrimination formula of the anomaly discrimination unit is as follows: ； Wherein, CV is the coefficient of difference, X_pd is the predicted data, X_rd is the real-time corporate carbon emission data, i represents any integer from 1 to n, n represents the total number of enterprises, k is the normal range value, S is the abnormal data discrimination result, and s1 and s2 represent discrimination result 1 and discrimination result 2, respectively. There are only two possible abnormal data discrimination results: discrimination result s1 and discrimination result s2. Discrimination result s1 indicates that the data difference is within the normal range, the real-time enterprise dual carbon data is not abnormal, and is output as normal data. Discrimination result s2 indicates that the data difference exceeds the normal range, the real-time enterprise dual carbon data is abnormal, and is output as abnormal data.

2. The carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data according to claim 1, characterized in that, The first output terminal of the data monitoring module is connected to the dual-carbon data center, and the second output terminal of the data monitoring module is connected to the carbon emission control system. The dual-carbon data center and the carbon emission control system operate independently and do not interfere with each other.

3. The carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data according to claim 2, characterized in that, The input end of the dual-carbon data center is connected to the output end of the data verification module, and the data stored in the dual-carbon data center includes normal data, abnormal data, raw data, and operation and maintenance data.

4. The carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data according to claim 3, characterized in that, The first input terminal of the data comparison unit is connected to the BP network model and is used to receive the prediction data transmitted by the BP network model. The second input terminal of the data comparison unit is connected to the data acquisition module and is used to receive the data transmitted in real time by the data acquisition module.

5. The carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data according to claim 4, characterized in that, The processing and analysis module includes a data processing unit, a data classification unit, and an anomaly analysis unit. The data processing unit is used to receive and process the abnormal data, the data classification unit is used to classify the data according to its characteristics, and the anomaly analysis unit is used to analyze the abnormal data. The anomaly analysis unit performs comparative analysis and trend analysis in sequence.

6. The carbon emission anomaly monitoring and analysis system capable of sorting and classifying abnormal data according to any one of claims 1-5, characterized in that, The usage process of the carbon emission anomaly monitoring and analysis system, which can sort and classify abnormal data, is as follows: Step 1: The prediction model creation module sends a request to the dual-carbon data center to obtain the original enterprise dual-carbon data, and establishes a BP network model based on the original enterprise dual-carbon data. Step 2: The data acquisition module acquires real-time enterprise dual-carbon data within the carbon emission control system and sends it to the data verification module. After receiving the real-time enterprise dual-carbon data and the predicted data transmitted by the BP network model, the data comparison unit compares the data to determine the data difference and sends it to the difference analysis unit. The difference analysis unit analyzes the data difference to determine its magnitude. Then, the difference discrimination unit determines whether the data difference is within the normal range based on the magnitude of the data difference, thereby determining whether the real-time enterprise dual-carbon data is abnormal. Step 3: The abnormal data is sent to the processing and analysis module. The data processing unit receives and processes the abnormal data. Then, the data classification module divides the data into categories according to its characteristics. Next, the anomaly analysis unit executes the comparative analysis and trend analysis commands in sequence to obtain the abnormal data analysis results. The abnormal data analysis results include the inherent patterns and trends of the abnormal data. Step 4: The control and management module sends control instructions to the enterprises corresponding to the abnormal data within the carbon emission control system based on the abnormal data analysis results, so that the enterprises corresponding to the abnormal data can make corresponding carbon emission adjustment and control according to the control instructions and promote the data to return to normal.

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