Data Processing Method and Device for Carbon Emission Monitoring System

Abstract

The present invention discloses a data processing method and device for a carbon emission monitoring system, which relates to the field of power data processing technology and is primarily intended to address the issues of poor accuracy and effectiveness in existing carbon emission monitoring. The method comprises: monitoring the data flow state of a power data processing subsystem, where the data flow state is used to characterize the progress of power data acquisition, processing, and storage; loading real-time power data and average power data from the power data processing subsystem if the data flow state meets a preset stability condition within a preset number of flow sampling times; determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, and generating a carbon emission monitoring curve based on the first and second carbon emission values; and displaying the carbon emission monitoring curve according to the flow time length of the data flow state.

Description

Technical Field

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

[0002] As my country strengthens its regulation of carbon emissions, power companies have adopted a threshold method to monitor carbon emissions generated during the power generation process in order to grasp the carbon emissions released by the power system in real time.

[0003] Currently, existing carbon emissions monitoring systems typically correlate calculations with power generation or supply data from the power system, and process these separately through the carbon emissions monitoring system to ensure data isolation between carbon emissions and the power system. However, since carbon emissions are calculated based on power data, there is a significant lag, and calculations require data to be loaded from the power system. This significantly impacts the timeliness of carbon emissions monitoring. Furthermore, the lag in data exchange between systems can affect the effectiveness of carbon emissions monitoring, and thus the accuracy of carbon emissions monitoring. Summary of the Invention

[0004] In view of this, the present invention provides a data processing method and device for a carbon emission monitoring system, the main purpose of which is to solve the problem of poor accuracy and effectiveness of existing carbon emission monitoring.

[0005] According to one aspect of the present invention, a data processing method for a carbon emission monitoring system is provided, comprising:

[0006] Monitor the data flow status of the power data processing subsystem, which is used to characterize the progress of power data collection, processing, and storage;

[0007] If the data flow state meets the preset stability condition within the preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded. The average power data is the average of the historical power data and the expected power data.

[0008] Determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, respectively, and generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value;

[0009] The carbon emission monitoring curve is displayed according to the flow time length of the data flow status.

[0010] Furthermore, monitoring the data flow status of the power data processing subsystem includes:

[0011] Sending a monitoring instruction to the power data processing subsystem so that the power data processing subsystem can retrieve the monitoring process feedback to collect, process and store the power data after passing the authority verification;

[0012] Determine the data flow status based on the data adjacency characteristics, data missing characteristics, and data identity characteristics in the process data;

[0013] It is determined whether the data flow state meets the preset stability conditions within a preset number of flow sampling times. The preset stability conditions include numerical stability conditions, amplitude stability conditions, and frequency stability conditions.

[0014] Furthermore, determining the data flow state based on the data adjacency feature, the data missing feature, and the data identity feature in the process data includes:

[0015] Extract data adjacency features, data missing features, and data identity features from process data;

[0016] Retrieve a feature score list and calculate the flow values of data adjacency, data missingness, and data identity based on the feature score list. The feature score list includes the correspondence between different features of collection, processing, and storage and different process scores.

[0017] According to the flow value, the data flow state is specified, and the data flow state includes a continuous flow state, an intermittent flow state, and an abrupt flow state.

[0018] Further, respectively determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data includes:

[0019] Based on the carbon emission prediction model that has completed model training, carbon emission prediction is performed on the real-time power data and the average power data respectively to obtain a first carbon emission value and a second carbon emission value;

[0020] Among them, the carbon emission prediction model is constructed based on a neural network and is trained based on electricity sample data with marked carbon emission measured values.

[0021] Furthermore, generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value includes:

[0022] determining a real-time plotting point based on the first carbon emission value, and determining an expected plotting point corresponding to a flow time length based on the second carbon emission;

[0023] Construct a carbon emission monitoring curve based on real-time plotting points and expected plotting points;

[0024] The carbon emission monitoring curve is displayed according to the flow time of the data flow status, including:

[0025] At least two historical drawing points corresponding to the flow time length are obtained, and a carbon emission expected monitoring area is generated based on the expected drawing points and the historical drawing points, and the carbon emission monitoring curve and the carbon emission expected monitoring area are displayed.

[0026] Furthermore, before loading the real-time power data and average power data in the power data processing subsystem, the method further includes:

[0027] A data loading request is sent to the power data processing subsystem, so that the power data processing subsystem determines the average power data and performs data preprocessing on the real-time power data.

[0028] Furthermore, the method further comprises:

[0029] If the data flow state does not meet the preset stability condition within the preset number of flow sampling times, the step of monitoring the data flow state of the power data processing subsystem is re-executed.

[0030] According to another aspect of the present invention, a data processing device for a carbon emission monitoring system is provided, comprising:

[0031] A monitoring module is used to monitor the data flow status of the power data processing subsystem. The data flow status is used to characterize the progress of power data collection, processing and storage;

[0032] A loading module is configured to load real-time power data and average power data from the power data processing subsystem if the data flow state meets a preset stability condition within a preset number of flow sampling times. The average power data is the average of the historical power data and the expected power data.

[0033] a determination module, configured to respectively determine a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, and generate a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value;

[0034] The display module is used to display the carbon emission monitoring curve according to the flow time length of the data flow state.

[0035] Furthermore, the monitoring module is specifically used to send a monitoring instruction to the power data processing subsystem, so that the power data processing subsystem can retrieve the process data of the monitoring process feedback to collect, process and store the power data after passing the authority verification; determine the data flow status based on the data adjacency characteristics, data missing characteristics, and data identity characteristics in the process data; and judge whether the data flow status meets the preset stability conditions within a preset number of flow sampling times, and the preset stability conditions include numerical stability conditions, amplitude stability conditions, and frequency stability conditions.

[0036] Furthermore, the monitoring module is specifically used to extract data adjacency features, data missing features, and data identity features from process data; retrieve a feature score list, and calculate the flow values of data adjacency features, data missing features, and data identity features based on the feature score list, wherein the feature score list includes the correspondence between different features collected, processed, and stored and different process scores; and specify the data flow state according to the flow value, wherein the data flow state includes a continuous flow state, an intermittent flow state, and an abrupt flow state.

[0037] Furthermore, a module is determined, which is specifically used to predict carbon emissions for real-time power data and average power data respectively based on a carbon emission prediction model that has completed model training, to obtain a first carbon emission value and a second carbon emission value; wherein, the carbon emission prediction model is constructed based on a neural network and is trained based on power sample data with marked carbon emission measured values.

[0038] Furthermore, the determination module is further configured to determine a real-time plotting point based on the first carbon emission value, and determine an expected plotting point corresponding to the flow time length based on the second carbon emission; and construct a carbon emission monitoring curve according to the real-time plotting point and the expected plotting point;

[0039] The display module is specifically used to obtain at least two historical drawing points corresponding to the flow time length, generate a carbon emission expected monitoring area based on the expected drawing points and the historical drawing points, and display the carbon emission monitoring curve and the carbon emission expected monitoring area.

[0040] Furthermore, the device further comprises:

[0041] The sending module is used to send a data loading request to the power data processing subsystem, so that the power data processing subsystem determines the average power data and performs data preprocessing on the real-time power data.

[0042] Furthermore, the execution module is configured to re-execute the step of monitoring the data flow state of the power data processing subsystem if the data flow state does not meet a preset stability condition within a preset number of flow sampling times.

[0043] According to another aspect of the present invention, a storage medium is provided, in which at least one executable instruction is stored. The executable instruction enables a processor to execute operations corresponding to the data processing method of the carbon emission monitoring system as described above.

[0044] According to another aspect of the present invention, there is provided a terminal comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0045] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the data processing method of the carbon emission monitoring system.

[0046] The beneficial effects of the present invention are:

[0047] The present invention provides a data processing method and device for a carbon emission monitoring system. Compared with the prior art, the embodiment of the present invention monitors the data flow status of the power data processing subsystem, and the data flow status is used to characterize the progress of power data collection, processing and storage; if the data flow status meets the preset stability conditions within a preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded, and the average power data is the average value of the power data at historical moments and the power data at expected moments; the first carbon emission value corresponding to the real-time power data and the second carbon emission value of the average power data are determined respectively, and a carbon emission monitoring curve is generated based on the first carbon emission value and the second carbon emission value; the carbon emission monitoring curve is displayed according to the flow time length of the data flow status, so as to achieve the purpose of effectively performing data interaction and carbon emission calculation under the premise of ensuring data isolation between the two subsystems, greatly improving the effectiveness of carbon emission calculation using power data, thereby improving the data processing accuracy of the carbon emission monitoring system.

[0048] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0050] Figure 1 A flow chart of a data processing method for a carbon emission monitoring system provided by an embodiment of the present invention is shown;

[0051] Figure 2 A schematic diagram of data interaction between subsystems provided by an embodiment of the present invention is shown;

[0052] Figure 3 A schematic diagram of a curve provided by an embodiment of the present invention is shown;

[0053] Figure 4 A schematic diagram of monitoring area rendering provided by an embodiment of the present invention is shown;

[0054] Figure 5A block diagram showing the composition of a data processing device of a carbon emission monitoring system provided by an embodiment of the present invention is shown;

[0055] Figure 6 A schematic structural diagram of a terminal provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0056] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0057] The embodiment of the present invention provides a data processing method for a carbon emission monitoring system, such as Figure 1 As shown, the method includes:

[0058] 101. Monitor the data flow status of the power data processing subsystem.

[0059] In the embodiment of the present application, the current execution subject, as the execution end that exchanges data with the power data processing subsystem and can perform carbon emission calculations, can be the carbon emission monitoring subsystem, or it can be embedded in a server together with the power data processing subsystem to isolate data functions. For example, in a master control server, the power data subsystem and the carbon emission monitoring subsystem are installed separately, and the two subsystems are connected through a data interface to realize data interaction between the two parties, such as Figure 2 As shown. After the current execution end exchanges data with the power data processing subsystem, the data flow status of the power data processing subsystem is monitored. At this time, the data flow status is used to characterize the progress of power data collection, processing, and storage, that is, the progress of power data collection, the progress of power data processing, and the progress of power data storage. The power data is the power supply data or transmission data collected by the power data processing subsystem for the power transmission equipment or power supply equipment, and the specific methods of collection, processing, and storage are not specifically limited in this embodiment of the application.

[0060] It should be noted that in the power data processing subsystem, the monitoring of data flow status can be judged by obtaining the power data in the power data processing subsystem, that is, monitoring is performed in the current execution end. Therefore, it does not affect the processing of power data in the power data processing subsystem, nor does it put pressure on system processing. Monitoring can be started only when carbon emission monitoring is required, thereby ensuring functional isolation between subsystems. In addition, the processing of power data in the power data processing subsystem may include but is not limited to power data statistics, power data prediction, etc., which are not specifically limited in the embodiments of this application.

[0061] 102. If the data flow state meets the preset stability condition within the preset number of flow sampling times, the real-time power data and the average power data in the power data processing subsystem are loaded.

[0062] In the embodiment of the present application, when the data flow state is obtained, the data flow state is judged to meet the preset stability conditions within the preset number of flow sampling times to determine whether the carbon emissions calculation can be performed. The preset number can be 3 or 5, and the flow sampling time is the length of time for sampling the power data in the power data processing subsystem, for example, 1 minute, 3 minutes, etc. The preset stability conditions include numerical stability conditions, amplitude stability conditions, and frequency stability conditions, so that the data flow state is judged to meet the preset stability conditions within the preset number of flow sampling times. If it meets the conditions, it means that the collection, storage, and processing of power data in the power data processing subsystem is stable. Therefore, real-time power data and average power data are loaded. At this time, the average power data is the average of the power data at the historical moment and the power data at the expected moment. The historical moment can be the historical moment of the previous or previous two sampling times, and the expected moment can be the expected moment of the next or next two sampling times. This is not specifically limited in the embodiment of the present application.

[0063] 103. Determine a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, respectively, and generate a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value.

[0064] In the embodiment of the present application, after the current execution end obtains the real-time power data and the average power data, it ensures the first carbon emission value corresponding to the real-time power data and the second carbon emission value corresponding to the average power data, so as to perform carbon emission monitoring based on the two carbon emission values. At this time, in order to facilitate the technician to view, the current execution end generates a carbon emission monitoring curve based on the first carbon emission value l1 and the second carbon emission value l2 for output display, such as Figure 3 shown.

[0065] It should be noted that in the embodiment of the present application, the carbon emission monitoring curve can display the real-time carbon emission value calculated based on the real-time power data and the expected carbon emissions calculated based on the average power data, so that technical personnel can conduct comparative monitoring.

[0066] 104. Display the carbon emission monitoring curve according to the flow time length of the data flow status.

[0067] In an embodiment of the present application, in order to better visualize the carbon emission monitoring curve, the current execution end displays the carbon emission monitoring curve according to the flow time length of the data flow state. At this time, the flow time length can be the length for the current execution end to monitor the data flow state, which can be pre-configured to 5 minutes or 10 minutes, etc. Preferably, the flow time length is greater than the flow sampling time, which is not specifically limited in the embodiment of the present application.

[0068] In another embodiment of the present application, for further definition and explanation, the step of monitoring the data flow status of the power data processing subsystem includes:

[0069] Sending monitoring instructions to the power data processing subsystem;

[0070] Determine the data flow status based on the data adjacency characteristics, data missing characteristics, and data identity characteristics in the process data;

[0071] Determine whether the data flow state meets the preset stability condition within a preset number of flow sampling times.

[0072] In order to realize the collection, storage, processing and other conditions of power data in the power data processing subsystem as the basis for accurate monitoring of carbon emissions, thereby improving the effectiveness of carbon emissions monitoring, when the current execution end monitors the data flow status of the power data processing subsystem, specifically, first sends a monitoring instruction to the power data processing subsystem. At this time, after the power data processing subsystem receives the monitoring instruction, it verifies the authority of the carbon emission monitoring subsystem of the current execution end, specifically whether the monitoring instruction carries a monitoring key, and determines whether the key is correct. If it is correct, it means that the current execution end has the authority to obtain power data. Therefore, the power data processing subsystem calls the monitoring process. At this time, the monitoring process is a process that is separately configured to obtain the situation of power data collection, processing and storage in the power data processing subsystem, so that the process data of collecting, processing and storing power data can be fed back through this monitoring process to ensure that other system processing contents of the power data processing subsystem are not affected when obtaining process data. The embodiment of this application does not make specific limitations.

[0073] After receiving the process data, the current execution end analyzes the data adjacency feature, data missing feature, and data identity feature in the process data to determine the data flow state based on the above three features, wherein the data adjacency feature is used to characterize whether each data is adjacent, the data identity feature is used to characterize whether each data is collected, processed or stored from a source, and the data missing feature is used to characterize whether each data corresponding to the collection, processing or storage node has data to determine the data flow state. At this time, the embodiment of the present application does not specifically limit the coherent flow state, intermittent flow state, and abrupt flow state. After obtaining the data flow state, it is determined whether the data flow state meets the preset stability conditions within a preset number of flow sampling times, wherein the preset stability conditions include numerical stability conditions, amplitude stability conditions, and frequency stability conditions. The numerical stability condition is used to limit the maximum and minimum values of the power data change, the amplitude stability condition is used to limit the maximum range and minimum range of the upper and lower floating differences of the power data, and the frequency stability condition is used to limit the number of times the power data exceeds the data stability condition or exceeds the amplitude stability condition within a specific time. The embodiment of the present application does not specifically limit it.

[0074] In another embodiment of the present application, for further definition and explanation, the step of determining the data flow state based on the data adjacency feature, the data missing feature, and the data identity feature in the process data includes:

[0075] Extract data adjacency features, data missing features, and data identity features from process data;

[0076] Retrieve the feature score list, and calculate the flow values of the data adjacency feature, data missing feature, and data identity feature based on the feature score list;

[0077] Specify the data flow status according to the flow value.

[0078] In order to determine the availability of power data based on the data flow state, thereby improving the accuracy and effectiveness of calculating carbon emissions based on power data, the current execution end extracts data adjacency features, data missing features, and data identity features from the process data. Among them, the process data includes the storage time, storage location, and storage identifier of the power data, the processing result, processing time, and processing identifier of the power data, and the collection time, collection content, and collection identifier of the power data. It can be judged whether the data are adjacent according to the storage time, processing time, and collection time of the power data. If adjacent, the data adjacency feature is adjacent. Correspondingly, it can be determined whether the power data is missing according to the storage location, processing result, and collection content of the power data. If missing, the data missing feature is missing. It can also be determined according to the storage identifier, collection identifier, and processing identifier corresponding to the storage, collection, and processing. That is, different identifiers correspond to the power data ID when collecting, processing, and storing. This embodiment of the application does not make specific limitations.

[0079] It should be noted that after the data adjacency feature, data missing feature, and data identity feature are currently determined, the current execution end retrieves the feature score list. At this time, the feature score list includes the corresponding relationship between different features collected, processed, and stored and different process scores. The process score is a score configured in advance for different features, and the score configuration is between 1-10. For example, in the feature score list, when the data adjacency feature is adjacent, it is configured as 7 points, when the data missing feature is not missing, it is configured as 8 points, and when the data identity feature is power supply time, it is configured as 5 points, etc. The embodiment of this application does not make specific restrictions. Then, the total score corresponding to the data adjacency feature, data missing feature, and data identity feature is used as the flow value based on the addition method. Furthermore, the data flow state is determined according to the specific data flow state of the flow value, that is, according to the pre-divided data flow state area. For example, more than 20 points is a continuous flow state, indicating that the power data is continuously collected, processed and stored; between 10 and 20 points is an intermittent flow state, indicating that the power data is continuous in large sections with occasional interruptions. It can be selected when the carbon emission value calculation is urgent; less than 10 points is an abrupt flow state, indicating that the power data has large interruptions, which is not conducive to the carbon emission value calculation.

[0080] In another embodiment of the present application, for further definition and explanation, the steps of respectively determining the first carbon emission value corresponding to the real-time power data and the second carbon emission value corresponding to the average power data include:

[0081] Based on the carbon emission prediction model that has completed model training, carbon emissions are predicted for the real-time power data and the average power data respectively to obtain a first carbon emission value and a second carbon emission value.

[0082] To achieve accurate carbon emission calculations and thus effective carbon emission monitoring based on electricity data, the current execution end pre-constructs a neural network for training as a learning model. Specifically, the carbon emission prediction model is constructed based on a neural network and trained based on electricity sample data labeled with actual carbon emission values. In this case, the electricity sample data includes power supply data corresponding to different times, and the actual carbon emission values are measured and collected in the area where the power supply equipment is located at the aforementioned different times. In addition, when training carbon emission values, the electricity sample data can be used as model input and the actual carbon emission values as model output for model training. In the following scenario, a multi-input and multi-output neural network can also be trained, that is, the electricity sample data and the economic data of the electricity consumption area during the corresponding time (such as electricity charges) are used as common input parameters to train the carbon emission prediction model. Finally, the real-time electricity data is predicted in turn to obtain the first carbon emission value and the average electricity data is predicted to obtain the second carbon emission value.

[0083] In another embodiment of the present application, for further definition and explanation, the step of generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value includes:

[0084] determining a real-time plotting point based on the first carbon emission value, and determining an expected plotting point corresponding to a flow time length based on the second carbon emission;

[0085] Construct a carbon emission monitoring curve based on real-time plotting points and expected plotting points;

[0086] To achieve visualization of the carbon emissions monitoring system, the current execution end generates a carbon emissions monitoring curve based on the first and second carbon emissions values. The first carbon emission value is used to determine a real-time plot point, l1. Simultaneously, based on the second carbon emission value, a predicted plot point, l2, is determined. The distance between the predicted plot point and the real-time plot point on the horizontal axis is the flow time length. The real-time plot point and the predicted plot point are then connected by a curve to generate the carbon emissions monitoring curve.

[0087] Correspondingly, the carbon emission monitoring curve displayed according to the flow time length of the data flow state includes:

[0088] At least two historical drawing points corresponding to the flow time length are obtained, and a carbon emission expected monitoring area is generated based on the expected drawing points and the historical drawing points, and the carbon emission monitoring curve and the carbon emission expected monitoring area are displayed.

[0089] In order to improve the display effect of the carbon emission monitoring curve, the current execution end obtains at least two historical drawing points according to the flow time length, such as l1 and l2, and then generates the carbon emission expected monitoring area based on the expected drawing point l4 and the historical drawing point and the real-time drawing point l3 to display the carbon emission monitoring curve and the carbon emission expected monitoring area, such as Figure 4 As shown, at this time, the real-time drawing point l3 is rendered separately. The left side of point l3 is the carbon emission value of the historical two flow time lengths, and the right side is the predicted carbon emission value, so that the carbon emission value is rendered and displayed in the form of a region, improving the visualization effect of carbon emission monitoring. In addition, after the carbon emission monitoring subsystem of the current execution end generates the carbon emission monitoring curve and the rendered image of the expected carbon emission monitoring area, it can be sent to the power data processing subsystem for display, such as through a floating window of the front-end interface, so that the carbon emission value can be directly viewed without entering the carbon emission monitoring subsystem. This embodiment of the application does not make specific limitations.

[0090] In another embodiment of the present application, for further definition and explanation, before the step of loading the real-time power data and the average power data in the power data processing subsystem, the method further includes:

[0091] A data loading request is sent to the power data processing subsystem, so that the power data processing subsystem determines the average power data and performs data preprocessing on the real-time power data.

[0092] In order to better interact with the power data processing subsystem for data calculation of carbon emission values, the current execution end sends a data loading request to the power data processing subsystem. When the power data processing subsystem receives the data loading request, it obtains the power data at the historical moment and the power data at the expected moment, and calculates the average value. Among them, the time step of the power data at the historical moment is the same as the time step of the power data at the expected moment. For example, the power data 5 minutes ago in history corresponds to the power data expected 5 minutes later. This embodiment of the present application does not make specific restrictions. In addition, the power data at the expected moment can be obtained by prediction in the power data processing subsystem, or it can be obtained by technical personnel based on power supply requirements. This embodiment of the present application does not make specific restrictions.

[0093] In another embodiment of the present application, for further definition and explanation, the steps further include:

[0094] If the data flow state does not meet the preset stability condition within the preset number of flow sampling times, the step of monitoring the data flow state of the power data processing subsystem is re-executed.

[0095] In order to improve the effectiveness of carbon emission value calculation based on power data, when the current execution end determines that the data flow status does not meet the preset stability conditions within the preset number of flow sampling times, it means that carbon emission prediction cannot be made based on the power data at this time. There are abnormal situations when the two subsystems interact with each other or when the power data processing subsystem processes, collects, and stores power data. Therefore, it is sufficient to re-execute the steps of monitoring the data flow status of the power data processing subsystem.

[0096] An embodiment of the present invention provides a data processing method for a carbon emission monitoring system. Compared with the prior art, the embodiment of the present invention monitors the data flow status of the power data processing subsystem, and the data flow status is used to characterize the progress of power data collection, processing and storage; if the data flow status meets the preset stability conditions within a preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded, and the average power data is the average value of the power data at historical moments and the power data at expected moments; the first carbon emission value corresponding to the real-time power data and the second carbon emission value of the average power data are determined respectively, and a carbon emission monitoring curve is generated based on the first carbon emission value and the second carbon emission value; the carbon emission monitoring curve is displayed according to the flow time length of the data flow status, so as to achieve the purpose of effectively performing data interaction and carbon emission calculation under the premise of ensuring data isolation between the two subsystems, greatly improving the effectiveness of carbon emission calculation using power data, thereby improving the data processing accuracy of the carbon emission monitoring system.

[0097] Furthermore, as a response to the above Figure 1 In order to realize the method shown in FIG, an embodiment of the present invention provides a data processing device for a carbon emission monitoring system, such as Figure 5 As shown, the device includes:

[0098] Monitoring module 21, used to monitor the data flow status of the power data processing subsystem, the data flow status is used to represent the progress of power data collection, processing and storage;

[0099] The loading module 22 is configured to load the real-time power data and average power data from the power data processing subsystem if the data flow state meets the preset stability condition within a preset number of flow sampling times. The average power data is the average of the historical power data and the expected power data.

[0100] a determination module 23 for respectively determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, and generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value;

[0101] The display module 24 is used to display the carbon emission monitoring curve according to the flow time length of the data flow state.

[0102] Furthermore, the monitoring module is specifically used to send a monitoring instruction to the power data processing subsystem, so that the power data processing subsystem can retrieve the process data of the monitoring process feedback to collect, process and store the power data after passing the authority verification; determine the data flow status based on the data adjacency characteristics, data missing characteristics, and data identity characteristics in the process data; and judge whether the data flow status meets the preset stability conditions within a preset number of flow sampling times, and the preset stability conditions include numerical stability conditions, amplitude stability conditions, and frequency stability conditions.

[0103] Furthermore, the monitoring module is specifically used to extract data adjacency features, data missing features, and data identity features from process data; retrieve a feature score list, and calculate the flow values of data adjacency features, data missing features, and data identity features based on the feature score list, wherein the feature score list includes the correspondence between different features collected, processed, and stored and different process scores; and specify the data flow state according to the flow value, wherein the data flow state includes a continuous flow state, an intermittent flow state, and an abrupt flow state.

[0104] Furthermore, a module is determined, which is specifically used to predict carbon emissions for real-time power data and average power data respectively based on a carbon emission prediction model that has completed model training, to obtain a first carbon emission value and a second carbon emission value; wherein, the carbon emission prediction model is constructed based on a neural network and is trained based on power sample data with marked carbon emission measured values.

[0105] Furthermore, the determination module is further configured to determine a real-time plotting point based on the first carbon emission value, and determine an expected plotting point corresponding to the flow time length based on the second carbon emission; and construct a carbon emission monitoring curve according to the real-time plotting point and the expected plotting point;

[0106] The display module is specifically used to obtain at least two historical drawing points corresponding to the flow time length, generate a carbon emission expected monitoring area based on the expected drawing points and the historical drawing points, and display the carbon emission monitoring curve and the carbon emission expected monitoring area.

[0107] Furthermore, the device further comprises:

[0108] The sending module is used to send a data loading request to the power data processing subsystem, so that the power data processing subsystem determines the average power data and performs data preprocessing on the real-time power data.

[0109] Furthermore, the execution module is configured to re-execute the step of monitoring the data flow state of the power data processing subsystem if the data flow state does not meet a preset stability condition within a preset number of flow sampling times.

[0110] An embodiment of the present invention provides a data processing device for a carbon emission monitoring system. Compared with the prior art, the embodiment of the present invention monitors the data flow status of the power data processing subsystem, and the data flow status is used to characterize the progress of power data collection, processing and storage; if the data flow status meets the preset stability conditions within a preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded, and the average power data is the average value of the power data at historical moments and the power data at expected moments; the first carbon emission value corresponding to the real-time power data and the second carbon emission value of the average power data are determined respectively, and a carbon emission monitoring curve is generated based on the first carbon emission value and the second carbon emission value; the carbon emission monitoring curve is displayed according to the flow time length of the data flow status, so as to achieve the purpose of effectively performing data interaction and carbon emission calculation under the premise of ensuring data isolation between the two subsystems, greatly improving the effectiveness of carbon emission calculation using power data, thereby improving the data processing accuracy of the carbon emission monitoring system.

[0111] According to one embodiment of the present invention, a storage medium is provided. The storage medium stores at least one executable instruction. The computer-executable instruction can execute the data processing method of the carbon emission monitoring system in any of the above method embodiments.

[0112] Figure 6 A schematic structural diagram of a terminal provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the terminal.

[0113] like Figure 6 As shown, the terminal may include: a processor 302 , a communications interface 304 , a memory 306 , and a communication bus 308 .

[0114] The processor 302 , the communication interface 304 , and the memory 306 communicate with each other via a communication bus 308 .

[0115] The communication interface 304 is used to communicate with other devices such as clients or other servers.

[0116] The processor 302 is configured to execute the program 310 , and specifically may execute the relevant steps in the embodiment of the data processing method for the carbon emission monitoring system.

[0117] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0118] Processor 302 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the terminal may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0119] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, or may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0120] The program 310 may be specifically configured to cause the processor 302 to perform the following operations:

[0121] Monitor the data flow status of the power data processing subsystem, which is used to characterize the progress of power data collection, processing, and storage;

[0122] If the data flow state meets the preset stability condition within the preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded. The average power data is the average of the historical power data and the expected power data.

[0123] Determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, respectively, and generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value;

[0124] The carbon emission monitoring curve is displayed according to the flow time length of the data flow status.

[0125] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A data processing method for a carbon emission monitoring system, characterized in that: include: Monitoring the data flow status of the power data processing subsystem, wherein the data flow status is used to characterize the progress of power data collection, processing, and storage; If the data flow state meets the preset stability condition within the preset number of flow sampling times, the real-time power data and average power data in the power data processing subsystem are loaded, and the average power data is the average value of the power data at the historical moment and the power data at the expected moment; respectively determining a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, and generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value; Displaying the carbon emission monitoring curve according to the flow time length of the data flow state; Monitoring the data flow status of the power data processing subsystem includes: Sending a monitoring instruction to the power data processing subsystem so that the power data processing subsystem, after passing the authority verification, retrieves the monitoring process feedback to collect, process and store the power data; Determining the data flow state based on data adjacency features, data missing features, and data identity features in the process data; Determining whether the data flow state meets preset stability conditions within a preset number of flow sampling times, the preset stability conditions including numerical stability conditions, amplitude stability conditions, and frequency stability conditions; The determining of the data flow state based on the data adjacency feature, the data missing feature, and the data identity feature in the process data includes: Extracting the data adjacency feature, the data missing feature, and the data identity feature from the process data; Retrieving a feature score list, and calculating the flow values of the data adjacency feature, the data missing feature, and the data identity feature based on the feature score list, wherein the feature score list includes a correspondence between different features of collection, processing, and storage and different process scores; The data flow state is determined according to the flow value, and the data flow state includes a continuous flow state, an intermittent flow state, and an abrupt flow state.

2. The method according to claim 1, characterized in that The determining of the first carbon emission value corresponding to the real-time power data and the second carbon emission value corresponding to the average power data respectively includes: Based on the carbon emission prediction model for which model training has been completed, carbon emission prediction is performed on the real-time power data and the average power data respectively to obtain the first carbon emission value and the second carbon emission value; The carbon emission prediction model is constructed based on a neural network and is trained based on power sample data marked with actual carbon emission values.

3. The method according to claim 1, characterized in that Generating a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value includes: determining a real-time plotting point based on the first carbon emission value, and determining an expected plotting point corresponding to the flow time length based on the second carbon emission; Constructing a carbon emission monitoring curve according to the real-time plotting points and the expected plotting points; The displaying of the carbon emission monitoring curve according to the flow time length of the data flow state includes: At least two historical drawing points corresponding to the flow time length are acquired, and a carbon emission expected monitoring area is generated based on the expected drawing points and the historical drawing points, and the carbon emission monitoring curve and the carbon emission expected monitoring area are displayed.

4. The method according to claim 1, wherein Before loading the real-time power data and average power data in the power data processing subsystem, the method further includes: A data loading request is sent to the power data processing subsystem, so that the power data processing subsystem determines the average power data and performs data preprocessing on the real-time power data.

5. The method according to claim 1, wherein The method further comprises: If the data flow state does not meet the preset stability condition within the preset number of flow sampling times, the step of monitoring the data flow state of the power data processing subsystem is re-executed.

6. A data processing device for a carbon emission monitoring system, characterized in that: include: A monitoring module, configured to monitor the data flow status of the power data processing subsystem, wherein the data flow status is used to characterize the progress of power data collection, processing, and storage; a loading module configured to load real-time power data and average power data from the power data processing subsystem if the data flow state meets a preset stability condition within a preset number of flow sampling times, wherein the average power data is an average of the power data at a historical moment and the power data at an expected moment; a determination module, configured to respectively determine a first carbon emission value corresponding to the real-time power data and a second carbon emission value corresponding to the average power data, and generate a carbon emission monitoring curve based on the first carbon emission value and the second carbon emission value; A display module, configured to display the carbon emission monitoring curve according to the flow time length of the data flow state; The monitoring module is specifically configured to send a monitoring instruction to the power data processing subsystem, so that the power data processing subsystem, after passing the authority verification, retrieves the monitoring process feedback to collect, process and store the power data; Determining the data flow state based on data adjacency features, data missing features, and data identity features in the process data; and determining whether the data flow state meets preset stability conditions within a preset number of flow sampling times, the preset stability conditions including numerical stability conditions, amplitude stability conditions, and frequency stability conditions; The monitoring module is further configured to extract the data adjacency feature, data missing feature, and data identity feature from the process data; Retrieve a feature score list, and calculate the flow values of the data adjacency feature, the data missing feature, and the data identity feature based on the feature score list, wherein the feature score list includes the correspondence between different features of collection, processing, and storage and different process scores; determine the data flow state according to the flow value, and the data flow state includes a continuous flow state, an intermittent flow state, and an abrupt flow state.

7. A storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the data processing method of the carbon emission monitoring system according to any one of claims 1 to 5.

8. A terminal, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the data processing method of the carbon emission monitoring system according to any one of claims 1 to 5.

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