Method, device and electronic equipment for handling carbon anomaly

Abstract

The application provides an energy-carbon abnormality processing method, device and electronic equipment. The method classifies energy-carbon data of each sub-region of each user terminal in an energy consumption region within a predetermined time period to obtain energy-carbon emission data and energy-carbon consumption data of each sub-region of each user terminal, processes the energy-carbon emission data and the energy-carbon consumption data of each sub-region of each user terminal to determine abnormal data of each user terminal, determines the identification of an abnormal sub-region corresponding to the abnormal data according to the abnormal data, and sends the identification of the abnormal sub-region to a server, so that the user terminal can know which sub-region is an abnormal sub-region, the analysis efficiency of the energy-carbon data is improved, and the problem of low analysis efficiency caused by manual analysis and processing of energy-carbon data analysis and management of each user terminal in an energy consumption region in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of coal energy consumption and carbon dioxide emission treatment technology, and more specifically, to a method, apparatus, computer-readable storage medium, and electronic device for handling energy and carbon anomalies. Background Technology

[0002] There are multiple user terminals in an industrial energy consumption area. Based on the energy carbon usage and emissions of each user terminal in the energy consumption area, it is necessary to analyze and manage the energy carbon data of each energy consumption area and carry out targeted rectification. However, the existing solution requires manual analysis and processing for the energy carbon data analysis and management of each user terminal in the energy consumption area, which is inefficient and has a high error rate. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, computer-readable storage medium, and electronic device for handling energy and carbon anomalies, so as to at least solve the problem that the analysis and management of energy and carbon data at each user terminal in the energy consumption area requires manual analysis and processing in the existing solution, resulting in low analysis efficiency.

[0004] To achieve the above objectives, according to one aspect of this application, an energy carbon anomaly processing method is provided. The method includes: acquiring energy carbon data for each sub-region of each user terminal within a predetermined time period to obtain multiple current energy carbon data sets; classifying the current energy carbon data for each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data for each sub-region of each user terminal, wherein the energy carbon emission data characterizes carbon dioxide emissions and the energy carbon consumption data characterizes energy consumption; determining abnormal data for each user terminal based at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, wherein the abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data; determining identifiers of abnormal sub-regions in all regions of each user terminal based on the abnormal data of each user terminal, and sending all identifiers of the abnormal sub-regions to a server.

[0005] Optionally, the current energy carbon data of each sub-region of each user terminal is classified to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. This includes: using the current energy carbon data of each sub-region of each user terminal as input to an energy carbon classification model, so that the energy carbon classification model processes the current energy carbon data and obtains the output of the energy carbon classification model. The energy carbon classification model is trained using multiple sets of training data, each set of training data including: energy carbon data acquired within a historical time period, and the energy carbon emission data and energy carbon consumption data corresponding to the energy carbon data; and determining that the output of the energy carbon classification model is the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal.

[0006] Optionally, abnormal data for each user terminal is determined based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, including: generating an energy carbon emission data curve based on all the energy carbon emission data; determining whether a first abrupt change point exists in the energy carbon emission data curve based on the energy carbon emission data curve, wherein the absolute value of the difference between the energy carbon emission data of two regions adjacent to the first abrupt change point is greater than a first difference threshold; and determining the energy carbon emission data corresponding to the first abrupt change point as the abnormal data if the first abrupt change point is determined to exist in the energy carbon emission data curve.

[0007] Optionally, the database stores a first mapping relationship, which is the energy carbon emission data and the corresponding sub-region identifier. Based on the abnormal data of each user terminal, the identifier of the abnormal sub-region in all regions of each user terminal is determined, including: based on the first mapping relationship and the energy carbon emission data corresponding to the first mutation point, determining the identifier of the sub-region corresponding to the first mutation point, and determining the identifier of the abnormal sub-region as the identifier of the sub-region corresponding to the first mutation point.

[0008] Optionally, abnormal data for each user terminal is determined based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, including: generating an energy carbon consumption data curve based on all the energy carbon consumption data; determining whether a second abrupt change point exists in the energy carbon consumption data curve based on the energy carbon consumption data curve, wherein the absolute value of the difference between the energy carbon consumption data of two regions adjacent to the second abrupt change point is greater than a second difference threshold; and determining the energy carbon consumption data corresponding to the second abrupt change point as the abnormal data if the second abrupt change point is determined to exist in the energy carbon consumption data curve.

[0009] Optionally, the database stores a second mapping relationship, which is the energy and carbon consumption data and the corresponding sub-region identifier. Determining the identifier of the sub-region corresponding to the second mutation point includes: determining the identifier of the sub-region corresponding to the second mutation point based on the second mapping relationship and the energy and carbon consumption data corresponding to the second mutation point, and determining the identifier of the abnormal sub-region as the identifier of the sub-region corresponding to the second mutation point.

[0010] Optionally, the method further includes:

[0011] according to To determine the energy quotas for each sub-region in the future time period.

[0012] Wherein, the energy quota is the total amount of available energy, E is the energy quota of each sub-region in the future time period, α, β and λ are the pre-set power supply coefficient, heating coefficient and quota coefficient respectively, Pe and Ph are the power supply power and heating power of each sub-region respectively, and Ey is the energy carbon consumption data of each sub-region.

[0013] According to another aspect of this application, an energy carbon anomaly processing device is provided, the device comprising:

[0014] The acquisition unit is used to acquire energy carbon data of each sub-region of each user terminal within the energy consumption area within a predetermined time period, and obtain multiple current energy carbon data.

[0015] The first processing unit is used to classify and process the current energy carbon data of each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. The energy carbon emission data is used to characterize the amount of carbon dioxide emitted, and the energy carbon consumption data is used to characterize the amount of energy consumed.

[0016] The second processing unit is configured to determine abnormal data for each user terminal based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, wherein the abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data.

[0017] The third processing unit is used to determine the identifiers of abnormal sub-regions in all areas of each user terminal based on the abnormal data of each user terminal, and send the identifiers of all abnormal sub-regions to the server.

[0018] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the described energy carbon anomaly processing methods.

[0019] According to another aspect of this application, an electronic device is provided, the electronic device including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any of the described energy carbon anomaly processing methods.

[0020] By applying the technical solution of this application, the energy carbon data of each sub-region of each user terminal within a predetermined time period is first classified to obtain the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. Then, the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are processed to identify the abnormal data of each user terminal. Finally, based on the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined and sent to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within the energy consumption area in the existing solution. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 A schematic flowchart of an energy carbon anomaly handling method according to an embodiment of this application is shown;

[0023] Figure 2 A schematic flowchart of another energy and carbon anomaly handling method provided according to an embodiment of this application is shown;

[0024] Figure 3 A structural block diagram of an energy carbon anomaly processing device provided according to an embodiment of this application is shown. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0029] Energy and carbon anomalies: abnormal energy consumption and abnormal carbon dioxide emissions.

[0030] As described in the background section, there are multiple user terminals in an industrial energy consumption area. Based on the energy carbon usage and emissions of each user terminal in the energy consumption area, it is necessary to analyze and manage the energy carbon data of each energy consumption area and carry out targeted rectification. However, in the existing solutions, the analysis and management of energy carbon data of each user terminal in the energy consumption area requires manual analysis and processing, which is inefficient and has a high error rate. In order to solve the problem of low analysis efficiency caused by the need for manual analysis and management of energy carbon data of each user terminal in the energy consumption area in the existing solutions, the embodiments of this application provide an energy carbon anomaly processing method, device, computer-readable storage medium and electronic device.

[0031] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0032] This embodiment provides a method for handling carbon anomalies. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1This is a schematic flowchart of a method for handling energy and carbon anomalies according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0034] Step S101: Obtain energy carbon data of each sub-region of each user terminal within the energy consumption area within a predetermined time period to obtain multiple current energy carbon data.

[0035] Step S102: Classify the current energy carbon data of each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. The energy carbon emission data is used to characterize the amount of carbon dioxide emitted, and the energy carbon consumption data is used to characterize the amount of energy consumed.

[0036] Step S102 involves classifying the current energy and carbon data for each sub-region of each of the aforementioned user terminals to obtain energy and carbon emission data and energy consumption data for each sub-region of each of the aforementioned user terminals, including:

[0037] The current energy carbon data of each sub-region of each of the aforementioned user terminals is used as input to the energy carbon classification model, so that the energy carbon classification model processes the current energy carbon data and obtains the output of the energy carbon classification model. The energy carbon classification model is trained using multiple sets of training data. Each set of training data includes energy carbon data acquired within a historical time period, as well as the corresponding energy carbon emission data and energy carbon consumption data. The output of the energy carbon classification model is determined to be the energy carbon emission data and energy carbon consumption data of each sub-region of each of the aforementioned user terminals.

[0038] Specifically, by constructing an energy carbon classification model, the model can process the input current energy carbon data, thereby classifying the current energy carbon data and outputting the energy carbon emission data and energy carbon consumption data of each sub-region of each of the aforementioned user terminals, achieving the goal of rapid classification.

[0039] In one embodiment of this application, abnormal data for each of the aforementioned user terminals is determined based on at least one of the aforementioned carbon emission data and the aforementioned carbon consumption data for each sub-region of each of the aforementioned user terminals, including:

[0040] Based on all the above energy carbon emission data, generate an energy carbon emission data curve;

[0041] The horizontal axis of the energy carbon emission data curve can be the sub-region number (a set number, not a unique identifier), and the vertical axis can be the energy carbon emission data.

[0042] Based on the above energy carbon emission data curve, determine whether there is a first abrupt change point in the above energy carbon emission data curve, wherein the absolute value of the difference between the above energy carbon emission data of two regions adjacent to the above first abrupt change point is greater than a first difference threshold.

[0043] Specifically, for example, if the energy carbon emission data of two adjacent regions of a point in the energy carbon emission data curve are 5 and 6 respectively, then the absolute value of the difference between the energy carbon emission data of the two adjacent regions of that point is 2, and the first difference threshold is 1. Then, that point is identified as the first mutation point, so that the mutation point can be found quickly, and then the corresponding abnormal data can be found quickly. The abnormal data in the energy carbon emission data are data with abnormal carbon dioxide emissions.

[0044] If the aforementioned first mutation point is found in the aforementioned carbon emission data curve, the aforementioned carbon emission data corresponding to the aforementioned first mutation point is determined to be the aforementioned abnormal data.

[0045] Step S102, namely, determining the abnormal data of each of the aforementioned user terminals based at least on one of the aforementioned carbon emission data and the aforementioned carbon consumption data for each sub-region of each of the aforementioned user terminals, including:

[0046] Based on all the above energy and carbon consumption data, generate an energy and carbon consumption data curve;

[0047] The horizontal axis of the energy and carbon consumption data curve can be the sub-region number (a set number, not a unique identifier), and the vertical axis can be the energy and carbon consumption data.

[0048] Based on the above energy and carbon consumption data curve, determine whether there is a second abrupt change point in the above energy and carbon consumption data curve, wherein the absolute value of the difference between the above energy and carbon consumption data of the two regions adjacent to the above second abrupt change point is greater than the second difference threshold.

[0049] Specifically, similar to the first mutation point, for example, if the energy and carbon consumption data of two adjacent regions of a point in the energy and carbon consumption data curve are 5 and 6 respectively, then the absolute value of the difference between the energy and carbon consumption data of the two adjacent regions of that point is 2, and the first difference threshold is 1. Therefore, this point is identified as the first mutation point, so that the mutation point can be found quickly, and then the corresponding abnormal data can be found quickly. The abnormal data in the energy and carbon consumption data are data with abnormal energy consumption.

[0050] If the second mutation point is found in the energy and carbon consumption data curve, the energy and carbon consumption data corresponding to the second mutation point is determined to be the abnormal data.

[0051] Step S103: Determine abnormal data for each of the aforementioned user terminals based at least on one of the aforementioned carbon emission data and the aforementioned carbon consumption data for each sub-region of each of the aforementioned user terminals. The abnormal data includes abnormal data in the aforementioned carbon emission data and / or abnormal data in the aforementioned carbon consumption data.

[0052] Specifically, after determining the first mutation point and the second mutation point, the abnormal data of each of the aforementioned user terminals is determined to include abnormal data in the aforementioned energy carbon emission data and abnormal data in the aforementioned energy carbon consumption data; after determining the first mutation point, the abnormal data of each of the aforementioned user terminals is determined to include abnormal data in the aforementioned energy carbon emission data; after determining the second mutation point, the abnormal data of each of the aforementioned user terminals is determined to include abnormal data in the aforementioned energy carbon consumption data.

[0053] Step S104: Based on the abnormal data from each of the aforementioned user terminals, determine the identifiers of all abnormal sub-regions in all areas of each of the aforementioned user terminals, and send all the identifiers of the aforementioned abnormal sub-regions to the server.

[0054] Specifically, the implementation method is as follows: the database stores a first mapping relationship, which is the energy carbon emission data and the corresponding sub-region identifier. Based on the first mapping relationship and the energy carbon emission data corresponding to the first mutation point, the identifier of the sub-region corresponding to the first mutation point is determined, and the identifier of the abnormal sub-region is determined as the identifier of the sub-region corresponding to the first mutation point.

[0055] In this way, by pre-storing a first mapping relationship, the identifier of the sub-region corresponding to the energy carbon emission data can be found according to the first mapping relationship. The identifier of the sub-region can represent the address identifier of the sub-region, thereby quickly identifying abnormal data in the energy carbon emission data.

[0056] The database stores a second mapping relationship, which is the energy carbon consumption data and the corresponding sub-region identifier. Based on the second mapping relationship and the energy carbon consumption data corresponding to the second mutation point, the identifier of the sub-region corresponding to the second mutation point is determined, and the identifier of the abnormal sub-region is determined as the identifier of the sub-region corresponding to the second mutation point.

[0057] In this way, by pre-storing a second mapping relationship, the identifier of the sub-region corresponding to the energy carbon consumption data can be found according to the second mapping relationship. The identifier of the sub-region can represent the address identifier of the sub-region, thereby quickly identifying abnormal data in the energy carbon consumption data.

[0058] In the above steps, the energy carbon data of each sub-region of each user terminal within the energy consumption area is first classified within a predetermined time period to obtain the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. Then, the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are processed to identify abnormal data of each user terminal. Finally, based on the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined and sent to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within the energy consumption area in the existing solution.

[0059] In one embodiment of this application, the method further includes:

[0060] according to The energy quota for each sub-region is determined in the future time period. In this embodiment, the future time period is set to 3 to 7 days, but it can also be set to other times according to actual needs. In this embodiment, 3 to 7 days is used as an example to illustrate that the energy quota for each sub-region can be adjusted in a timely manner so that the adjusted energy quota can match the energy usage of each sub-region.

[0061] In this context, the energy quota refers to the total available energy, E represents the energy quota for each sub-region in the future time period, α, β, and λ are the pre-set power supply coefficient, heating coefficient, and quota coefficient, respectively, which are set by the user-end management personnel according to actual needs. Pe and Ph represent the power supply and heating power of each sub-region, respectively, and Ey represents the historical energy carbon consumption data of each sub-region. When calculating the energy quota for each sub-region, the daily energy carbon consumption over a historical period from the current time is obtained. The average daily energy carbon consumption over this historical period is calculated, and the historical energy carbon consumption data is determined by combining the average energy carbon consumption with the set future time period, thus obtaining the energy carbon usage of each sub-region over a historical period. The historical period is a preset time period. For example, if the future time period is set to 7 days, the daily energy carbon consumption from the current day to the seventh day is obtained, and the average daily energy carbon consumption is calculated (that is, the sum of the daily energy carbon consumption from the current day to the seventh day divided by 7). Multiplying the average daily energy carbon consumption by 7 gives the historical energy carbon consumption data for each sub-region (or the sum of the daily energy carbon consumption from the current day to the seventh day).

[0062] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the energy and carbon anomaly processing method of this application will be described in detail below with reference to specific embodiments.

[0063] This embodiment relates to a specific method for handling energy and carbon anomalies, such as... Figure 2 As shown, it includes the following steps:

[0064] Step S1: Obtain energy and carbon data for each unit within the energy consumption area;

[0065] Step S2: Classify the energy and carbon data of each user terminal in the energy consumption area according to the energy and carbon analysis model to obtain the energy consumption data and carbon emission data of each user terminal.

[0066] The above energy carbon classification model was trained using multiple sets of training data. Each set of training data includes energy carbon data acquired within a historical time period, as well as the energy carbon emission data and energy carbon energy consumption data corresponding to the above energy carbon data.

[0067] Step S3: Obtain the preset area of ​​each user terminal;

[0068] Step S4: Based on the preset area, the energy consumption address identifier of the energy consumption data and the emission address identifier of the carbon emission data, classify the energy consumption data and carbon emission data to obtain the regional energy consumption data and regional carbon emission data of each user terminal.

[0069] Step S5: Analyze the regional energy consumption data and regional carbon emission data of each user terminal to obtain emission analysis data and energy consumption analysis data of each sub-region of each user terminal;

[0070] Step S6: Analyze the emission analysis data and energy consumption analysis data to obtain the test results;

[0071] Step S7: When abnormal data is found in the carbon energy data characterized by the detection results, the abnormal data is determined based on the detection results;

[0072] Step S8: Send the abnormal data and energy carbon data to the server of the user terminal where the energy carbon data contains abnormal data.

[0073] Curves were plotted for energy consumption and carbon emission data separately. Peak points and abrupt changes in the energy consumption curve were identified, and the curve was divided into time periods to determine the total energy consumption within each period. Simultaneously, energy-consuming areas were identified within the energy consumption regions based on the address identification information attached to the energy consumption data. Similarly, peak points and abrupt changes in the carbon emission curve were identified, and the curve was divided into time periods to determine the total carbon emissions within each period. A deep learning model based on an attention mechanism was used for trend prediction. An encoder module was used to collaboratively encode the obtained carbon emission data, along with multi-dimensional data such as temperature, humidity, electricity consumption, water consumption, gas consumption, and heat consumption. This is used to obtain feature information under long sequences, and to perform feature recognition and decoding on carbon emission data through a decoder to determine the carbon emission trend and its correlation with other data in the future. The slope of the curve is calculated to determine the carbon emission intensity in the sub-region, and emission analysis data and energy consumption analysis data are obtained. The emission analysis data includes, but is not limited to, the total carbon emissions of each sub-region in each user terminal within a preset time period, the emission curve of each region, emission intensity, emission inflection point, emission peak and emission trend; the energy consumption analysis data includes, but is not limited to, the total energy consumption of each sub-region in each user terminal within a preset time period, the energy consumption area of ​​each sub-region, energy consumption inflection point and energy consumption peak.

[0074] The steps for trend prediction using a deep learning model based on an attention mechanism are as follows:

[0075] Step 1: Scale the carbon emission data to [0,1] using the range normalization method;

[0076] Step 2: Create a series of continuous time series samples using the sliding window method. The data in each window serves as input, and one or more subsequent data points serve as the target output. The sliding window method is specifically expressed as follows:

[0077] Given a data length of Time series, with a defined window length of... It can create ( - ) input-output sample pairs, created by, for each sliding window , where (1≤ ≤ - ), input sample (, - .=[, - .,, - +1 .,...,, - + -1 .]), output sample(, - .=[, - + .,, - + +1 .,...,, - + + -1 .]),in This is the prediction step size. In this way, the sliding window generates a dataset that can be directly used for training, with each sample representing the past... Data from various points in time can be used to predict the future. Data at each point in time;

[0078] Step 3: Use a deep learning model with an attention mechanism. The attention mechanism selectively focuses on features and calculates attention weights to achieve model learning and optimization. In this model, the current time sequence data serves as both the output of the decoder for the previous time period and the input of the Query (Q) for the decoder for the next time period, to explore the weights of different data dimensions and parameters in energy consumption and carbon emission data prediction. The specific model settings are as follows:

[0079] The model training parameters are set as follows: the batch size of training samples is set to 64, the sliding window size is set to one hour, and the training features in this method include data from six dimensions: temperature, humidity, electricity consumption, water consumption, gas consumption, and heat consumption.

[0080] The model's method is specifically expressed as follows: First, given an input sequence... Each time step There is dimensional vector - . The length of the sequence is Therefore, the input matrix The dimension is × Secondly, the attention weights are calculated as follows: the line transforms the input matrix through a linear layer to obtain three matrices: ( ), ( )and ( ): -= , - ,.- -= , - ,.- -= , - ..,in, - ., - , - . It is the weight matrix to be learned;

[0081] Next, use and Calculate the attention score: Score( , =, ,here,, - .yes The dimension of a vector, K T Let K be the transpose of the matrix. It is used for scaling to ensure that the scores do not become too large or too small when calculating attention weights due to excessive dimensionality.

[0082] Then, the normalization function is applied. Obtain the normalized attention weights: Attention( , , =softmax(Score( , ))× Finally, the resulting matrix is ​​the weighted matrix. Matrix, dimension × This is the output after processing by the self-attention mechanism. The weighted value matrix can reflect the data in each dimension that plays an important role in predicting energy consumption and carbon emission data.

[0083] Step 4: After training the model, use the validation dataset to verify the accuracy of the predictions.

[0084] By first classifying the energy carbon data of each sub-region of each user terminal within a predetermined time period within the energy consumption area, energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are obtained. Then, the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are processed to identify abnormal data of each user terminal. Finally, based on the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined and sent to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within the energy consumption area in the existing solution.

[0085] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0086] This application also provides an energy and carbon anomaly processing device. It should be noted that this energy and carbon anomaly processing device can be used to execute the energy and carbon anomaly processing method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] The following describes the energy and carbon anomaly handling device provided in the embodiments of this application.

[0088] Figure 3 This is a structural block diagram of an energy carbon anomaly processing device provided according to an embodiment of this application. Figure 3 As shown, the device includes:

[0089] The acquisition unit 31 is used to acquire the energy carbon data of each sub-area of ​​each user terminal within the energy consumption area within a predetermined time period, and obtain multiple current energy carbon data.

[0090] The first processing unit 32 is used to classify and process the current energy carbon data of each sub-region of each of the above-mentioned user terminals to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each of the above-mentioned user terminals. The energy carbon emission data is used to characterize the amount of carbon dioxide emitted, and the energy carbon consumption data is used to characterize the amount of energy consumed.

[0091] The second processing unit 33 is used to determine abnormal data for each of the aforementioned user terminals based at least on one of the aforementioned carbon emission data and the aforementioned carbon consumption data for each sub-region of each of the aforementioned user terminals. The abnormal data includes abnormal data in the aforementioned carbon emission data and / or abnormal data in the aforementioned carbon consumption data.

[0092] The third processing unit 34 is used to determine the identifiers of abnormal sub-regions in all areas of each of the aforementioned user terminals based on the aforementioned abnormal data of each of the aforementioned user terminals, and send the identifiers of all the aforementioned abnormal sub-regions to the server.

[0093] In the aforementioned device, the energy carbon data of each sub-region of each user terminal within a predetermined time period is first classified to obtain the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. Then, the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are processed to identify abnormal data of each user terminal. Finally, based on the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined and sent to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within the energy consumption area in the existing solution.

[0094] In one embodiment of this application, the first processing unit includes a first processing module and a second processing module. The first processing module is used to take the current energy carbon data of each sub-region of each of the aforementioned user terminals as input to the energy carbon classification model, so that the energy carbon classification model processes the current energy carbon data and obtains the output of the energy carbon classification model. The energy carbon classification model is trained using multiple sets of training data. Each set of training data includes energy carbon data acquired within a historical time period, as well as energy carbon emission data and energy carbon consumption data corresponding to the energy carbon data. The output of the energy carbon classification model is determined to be the energy carbon emission data and energy carbon consumption data of each sub-region of each of the aforementioned user terminals.

[0095] In one embodiment of this application, the second processing unit includes a first generation module, a first determination module, and a second determination module. The first generation module is used to generate an energy carbon emission data curve based on all the aforementioned energy carbon emission data. The first determination module is used to determine whether there is a first abrupt change point in the energy carbon emission data curve based on the aforementioned energy carbon emission data curve, wherein the absolute value of the difference between the energy carbon emission data of two regions adjacent to the aforementioned first abrupt change point is greater than a first difference threshold. The second determination module is used to determine, if it is determined that there is the aforementioned first abrupt change point in the aforementioned energy carbon emission data curve, that the energy carbon emission data corresponding to the aforementioned first abrupt change point is the aforementioned abnormal data.

[0096] In one embodiment of this application, a first mapping relationship is stored in the database. The first mapping relationship is the carbon emission data and the corresponding sub-region identifier. Based on the abnormal data of each user terminal, the identifier of the abnormal sub-region in all regions of each user terminal is determined, including: based on the first mapping relationship and the carbon emission data corresponding to the first mutation point, the identifier of the sub-region corresponding to the first mutation point is determined, and the identifier of the abnormal sub-region is determined to be the identifier of the sub-region corresponding to the first mutation point.

[0097] In one embodiment of this application, determining abnormal data for each of the aforementioned user terminals based on at least one of the aforementioned energy carbon emission data and the aforementioned energy carbon consumption data for each sub-region of each of the aforementioned user terminals includes: generating an energy carbon consumption data curve based on all the aforementioned energy carbon consumption data; determining whether a second abrupt change point exists in the aforementioned energy carbon consumption data curve based on the aforementioned energy carbon consumption data curve, wherein the absolute value of the difference between the energy carbon consumption data of two regions adjacent to the aforementioned second abrupt change point is greater than a second difference threshold; and determining that the energy carbon consumption data corresponding to the aforementioned second abrupt change point is the aforementioned abnormal data if the existence of the aforementioned second abrupt change point is determined in the aforementioned energy carbon consumption data curve.

[0098] In one embodiment of this application, a second mapping relationship is stored in the database. The second mapping relationship is the energy and carbon consumption data and the corresponding sub-region identifier. Determining the identifier of the sub-region corresponding to the second mutation point includes: determining the identifier of the sub-region corresponding to the second mutation point based on the second mapping relationship and the energy and carbon consumption data corresponding to the second mutation point, and determining the identifier of the abnormal sub-region as the identifier of the sub-region corresponding to the second mutation point.

[0099] In one embodiment of this application, the second processing unit includes a first generation module, a first determination module, and a second determination module. The first generation module is used to generate an energy carbon emission data curve based on all the aforementioned energy carbon emission data. The first determination module is used to determine whether there is a first abrupt change point in the energy carbon emission data curve based on the aforementioned energy carbon emission data curve, wherein the absolute value of the difference between the energy carbon emission data of two regions adjacent to the aforementioned first abrupt change point is greater than a first difference threshold. The second determination module is used to determine the identifier of the sub-region corresponding to the aforementioned first abrupt change point when it is determined that the aforementioned energy carbon emission data curve contains the aforementioned first abrupt change point.

[0100] In one embodiment of this application, a first mapping relationship is stored in the database. The first mapping relationship is the energy carbon emission data and the identifier of the corresponding sub-region. The second determining module includes a first determining sub-module, which is used to determine the identifier of the sub-region corresponding to the first mutation point based on the first mapping relationship and the energy carbon emission data corresponding to the first mutation point.

[0101] In one embodiment of this application, the second processing unit includes a second generation module, a third determination module, and a fourth determination module. The second generation module is used to generate an energy-carbon consumption data curve based on all the aforementioned energy-carbon consumption data. The third determination module is used to determine whether there is a second abrupt change point in the energy-carbon consumption data curve based on the aforementioned energy-carbon consumption data curve, wherein the absolute value of the difference between the energy-carbon consumption data of two regions adjacent to the aforementioned second abrupt change point is greater than a second difference threshold. The fourth determination module is used to determine the identifier of the sub-region corresponding to the aforementioned second abrupt change point when it is determined that there is the aforementioned second abrupt change point in the aforementioned energy-carbon consumption data curve.

[0102] In one embodiment of this application, a second mapping relationship is stored in the database. The second mapping relationship is the energy consumption data and the identifier of the corresponding sub-region. The fourth determining module includes a second determining sub-module, which is used to determine the identifier of the sub-region corresponding to the second mutation point based on the second mapping relationship and the energy consumption data corresponding to the second mutation point.

[0103] In one embodiment of this application, the above-described apparatus further includes a fourth processing unit.

[0104] The fourth processing unit is used to... To determine the energy quotas for each sub-region in the future time period.

[0105] Wherein, the above-mentioned energy quota is the total amount of available energy, E is the above-mentioned energy quota of each sub-region in the future time period, α, β and λ are the pre-set power supply coefficient, heating coefficient and quota coefficient respectively, Pe and Ph are the power supply and heating power of each sub-region respectively, and Ey is the above-mentioned energy carbon consumption data of each sub-region.

[0106] The aforementioned carbon anomaly processing device includes a processor and a memory. The acquisition unit, first processing unit, second processing unit, and third processing unit are all stored as program units in the memory. The processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0107] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem of low analysis efficiency in existing solutions where energy and carbon data analysis and management at various user terminals within an energy consumption area requires manual processing.

[0108] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0109] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the energy carbon anomaly handling method.

[0110] This invention provides a processor for running a program, wherein the program executes the aforementioned carbon anomaly handling method during runtime.

[0111] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: acquiring energy carbon data for each sub-region of each user terminal within a predetermined time period, obtaining multiple current energy carbon data; classifying the current energy carbon data for each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data for each sub-region of each user terminal, wherein the energy carbon emission data characterizes carbon dioxide emissions and the energy carbon consumption data characterizes energy consumption; determining abnormal data for each user terminal based at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, wherein the abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data; determining the identifiers of abnormal sub-regions in all regions of each user terminal based on the abnormal data of each user terminal, and sending all the identifiers of the abnormal sub-regions to a server. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0112] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: acquiring energy carbon data of each sub-region of each user terminal within a predetermined time period to obtain multiple current energy carbon data; classifying the current energy carbon data of each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal, wherein the energy carbon emission data is used to characterize the amount of carbon dioxide emitted and the energy carbon consumption data is used to characterize the amount of energy consumed; determining abnormal data of each user terminal based at least one of the energy carbon emission data and the energy carbon consumption data of each sub-region of each user terminal, wherein the abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data; determining the identifiers of abnormal sub-regions in all regions of each user terminal based on the abnormal data of each user terminal, and sending all the identifiers of the abnormal sub-regions to a server.

[0113] This application also provides an electronic device, which includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for executing any of the above-described energy carbon anomaly processing methods. By first classifying the energy carbon data of each sub-region of each user terminal within a predetermined time period within an energy consumption area, energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are obtained. Then, the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal are processed to determine the abnormal data of each user terminal. Finally, based on the abnormal data, the identifier of the abnormal sub-region corresponding to the abnormal data is determined, and the identifier of the abnormal sub-region is sent to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within an energy consumption area in existing solutions.

[0114] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they 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.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0120] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0121] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0122] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0124] 1) The energy carbon anomaly processing method of this application first classifies the energy carbon data of each sub-region of each user terminal within a predetermined time period within the energy consumption area to obtain the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. Then, it processes the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal to determine the abnormal data of each user terminal. Finally, based on the abnormal data, it determines the identifier of the abnormal sub-region corresponding to the abnormal data and sends the identifier of the abnormal sub-region to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy carbon data analysis and management of each user terminal within the energy consumption area in the existing solution.

[0125] 2) The energy and carbon anomaly processing device of this application first classifies the energy and carbon data of each sub-region of each user terminal within a predetermined time period in the energy consumption area to obtain the energy and carbon emission data and energy consumption data of each sub-region of each user terminal. Then, it processes the energy and carbon emission data and energy consumption data of each sub-region of each user terminal to determine the abnormal data of each user terminal. Finally, based on the abnormal data, it determines the identifier of the abnormal sub-region corresponding to the abnormal data and sends the identifier of the abnormal sub-region to the server so that the user terminal can know which sub-regions are abnormal. This improves the efficiency of energy and carbon data analysis and solves the problem of low analysis efficiency caused by the need for manual analysis and processing of energy and carbon data analysis and management of each user terminal in the energy consumption area in the existing solution.

[0126] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for handling energy carbon anomalies, characterized in that, include: Obtain energy carbon data for each sub-region of each user terminal within a predetermined time period within the energy consumption area, and obtain multiple current energy carbon data. The current energy and carbon data of each sub-region of each user terminal are classified and processed to obtain energy and carbon emission data and energy and carbon consumption data of each sub-region of each user terminal. The energy and carbon emission data is used to characterize the amount of carbon dioxide emitted, and the energy and carbon consumption data is used to characterize the amount of energy consumed. Abnormal data for each user terminal is determined based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal. The abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data. Based on the abnormal data from each user terminal, determine the identifiers of the abnormal sub-regions in all areas of each user terminal, and send all the identifiers of the abnormal sub-regions to the server. Based on all the energy and carbon consumption data, generate an energy and carbon consumption data curve; Based on the energy carbon consumption data curve, determine whether there is a second abrupt change point in the energy carbon consumption data curve, wherein the absolute value of the difference between the energy carbon consumption data of two regions adjacent to the second abrupt change point is greater than the second difference threshold. If it is determined that the second mutation point exists in the energy carbon consumption data curve, the energy carbon consumption data corresponding to the second mutation point is determined to be the abnormal data. according to The energy quota for each sub-region in the future time period is determined, wherein the energy quota is the total amount of available energy, E is the energy quota for each sub-region in the future time period, α, β and λ are the pre-set power supply coefficient, heating coefficient and quota coefficient respectively, Pe and Ph are the power supply power and heating power of each sub-region respectively, and Ey is the energy carbon consumption data of each sub-region.

2. The method according to claim 1, characterized in that, The current energy and carbon data of each sub-region of each user terminal are classified and processed to obtain energy and carbon emission data and energy consumption data of each sub-region of each user terminal, including: The current energy carbon data of each sub-region of each user terminal is used as the input of the energy carbon classification model, so that the energy carbon classification model processes the current energy carbon data and obtains the output of the energy carbon classification model. The energy carbon classification model is trained using multiple sets of training data. Each set of training data includes energy carbon data acquired within a historical time period, as well as the energy carbon emission data and energy carbon energy consumption data corresponding to the energy carbon data. The output of the energy carbon classification model is determined to be the energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal.

3. The method according to claim 1, characterized in that, Abnormal data for each user terminal is determined based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, including: Based on all the energy carbon emission data, generate an energy carbon emission data curve; Based on the energy carbon emission data curve, determine whether there is a first abrupt change point in the energy carbon emission data curve, wherein the absolute value of the difference between the energy carbon emission data of two regions adjacent to the first abrupt change point is greater than a first difference threshold. If the first mutation point is found in the energy carbon emission data curve, the energy carbon emission data corresponding to the first mutation point is determined to be the abnormal data.

4. The method according to claim 3, characterized in that, The database stores a first mapping relationship, which is the carbon emission data and the corresponding sub-region identifiers. Based on the abnormal data from each user terminal, the identifiers of abnormal sub-regions in all regions of each user terminal are determined, including: Based on the first mapping relationship and the carbon emission data corresponding to the first mutation point, the identifier of the sub-region corresponding to the first mutation point is determined, and the identifier of the abnormal sub-region is determined to be the identifier of the sub-region corresponding to the first mutation point.

5. The method according to claim 1, characterized in that, The database stores a second mapping relationship, which is the energy carbon consumption data and the corresponding sub-region identifiers. Determining the identifier of the sub-region corresponding to the second mutation point includes: Based on the second mapping relationship and the energy consumption data corresponding to the second mutation point, the identifier of the sub-region corresponding to the second mutation point is determined, and the identifier of the abnormal sub-region is determined as the identifier of the sub-region corresponding to the second mutation point.

6. An energy carbon anomaly processing device, characterized in that, include: The acquisition unit is used to acquire energy carbon data of each sub-region of each user terminal within the energy consumption area within a predetermined time period, and obtain multiple current energy carbon data. The first processing unit is used to classify and process the current energy carbon data of each sub-region of each user terminal to obtain energy carbon emission data and energy carbon consumption data of each sub-region of each user terminal. The energy carbon emission data is used to characterize the amount of carbon dioxide emitted, and the energy carbon consumption data is used to characterize the amount of energy consumed. The second processing unit is configured to determine abnormal data for each user terminal based on at least one of the energy carbon emission data and the energy carbon consumption data for each sub-region of each user terminal, wherein the abnormal data includes abnormal data in the energy carbon emission data and / or abnormal data in the energy carbon consumption data. The third processing unit is used to determine the identifier of the abnormal sub-region in all areas of each user terminal based on the abnormal data of each user terminal, and send the identifier of all the abnormal sub-regions to the server. The second generation module is used to generate an energy-carbon consumption data curve based on all the energy-carbon consumption data. The third determining module is used to determine whether there is a second abrupt change point in the energy carbon consumption data curve based on the energy carbon consumption data curve, wherein the absolute value of the difference between the energy carbon consumption data of two regions adjacent to the second abrupt change point is greater than a second difference threshold. The fourth determining module is used to determine the energy and carbon consumption data corresponding to the second mutation point as the abnormal data when it is determined that there is a second mutation point in the energy and carbon consumption data curve; The fourth processing unit is used to process according to The energy quota for each sub-region in the future time period is determined, wherein the energy quota is the total amount of available energy, E is the energy quota for each sub-region in the future time period, α, β and λ are the pre-set power supply coefficient, heating coefficient and quota coefficient respectively, Pe and Ph are the power supply power and heating power of each sub-region respectively, and Ey is the energy carbon consumption data of each sub-region.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the energy and carbon anomaly handling method according to any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing the energy carbon anomaly processing method according to any one of claims 1 to 5.

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