Carbon emission early warning method, device, equipment, storage medium and program product

Abstract

The invention relates to a carbon emission early warning method and device, equipment, a storage medium and a program product. The method comprises the following steps: firstly, acquiring historical energy data including historical power consumption data and historical carbon emission data of a target area, then, determining a historical carbon emission conversion coefficient according to the historical power consumption data and the historical carbon emission data, and then, calculating the historical carbon emission conversion coefficient according to the historical carbon emission conversion coefficient. The method comprises the steps of obtaining a historical carbon emission conversion coefficient, conducting prediction according to the historical carbon emission conversion coefficient to obtain a target carbon emission conversion coefficient, further determining target carbon emission data according to the target carbon emission conversion coefficient, and finally conducting carbon emission early warning on a target area according to the target carbon emission data and a carbon emission early warning value. By adopting the method, quantitative prediction and early warning of carbon emission can be realized, and the accuracy of carbon emission early warning is higher.

Description

Technical Field

[0001] The present application relates to the field of electric power grid technology, and in particular to a carbon emission early warning method, device, equipment, storage medium and program product. Background Art

[0002] Carbon emission early warning technology can identify and warn of possible carbon emission violations in industrial production, transportation, energy consumption, and other activities. In the field of power systems, carbon emission early warning technology, as an important supporting tool for the low-carbon transformation of power systems, can monitor and identify abnormal fluctuations in emission levels in real time, and provide early warnings of potential risks of exceeding standards, providing a basis for the rapid deployment of low-carbon measures such as scheduling strategy adjustments, energy storage regulation, and demand response. At the planning and evaluation level, it can also support the comparative analysis of the carbon trajectories of different options during the planning stage, helping decision makers achieve an efficient and low-carbon transformation of the power system while ensuring system safety and economy.

[0003] At present, commonly used carbon emission warning technologies include methods based on statistical learning, such as logistic regression, K-nearest neighbor and fuzzy theory, as well as methods based on machine learning and deep learning, such as XGBoost, support vector machine, long short-term memory network (LSTM), etc. However, the accuracy of carbon emission warnings using these methods is not high. Summary of the Invention

[0004] Based on this, it is necessary to provide a carbon emission early warning method, device, equipment, storage medium and program product that can improve accuracy in response to the above technical problems.

[0005] In a first aspect, the present application provides a carbon emission early warning method, comprising:

[0006] Obtain historical energy data for the target area, including historical electricity consumption data and historical carbon emission data;

[0007] Determine the historical carbon emission conversion coefficient based on historical electricity consumption data and historical carbon emission data;

[0008] The target carbon emission conversion coefficient is obtained by forecasting based on the historical carbon emission conversion coefficient;

[0009] Determine the target carbon emission data based on the target carbon emission conversion coefficient;

[0010] Based on the target carbon emission data and carbon emission warning values, carbon emission warnings are issued for the target areas.

[0011] In one embodiment, determining target carbon emission data according to the target carbon emission conversion coefficient includes:

[0012] Target power consumption data is obtained by forecasting based on historical power consumption data;

[0013] The target carbon emission data is determined based on the target carbon emission conversion coefficient and the target electricity consumption data.

[0014] In one embodiment, before determining the historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data, the method further includes:

[0015] If the time frequencies of the historical electricity consumption data and the historical carbon emission data are inconsistent, the historical electricity consumption data and the historical carbon emission data are subjected to frequency conversion processing.

[0016] In one embodiment, before predicting the target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient, the method further includes:

[0017] Conduct a stability test on the historical carbon emission conversion coefficient;

[0018] When the historical carbon emission conversion coefficient is a non-stationary sequence, the historical carbon emission conversion coefficient is transformed to achieve stationarity.

[0019] In one embodiment, determining target carbon emission data based on the target carbon emission conversion coefficient and target electricity consumption data includes:

[0020] The linear product of the target carbon emission conversion coefficient and the target electricity consumption data is determined as the target carbon emission data.

[0021] In one embodiment, a carbon emission warning is issued to a target area based on the target carbon emission data and the carbon emission warning value, including:

[0022] Determine the carbon emission warning value within the warning period based on historical carbon emission data and the maximum emission threshold of the target period;

[0023] Based on the target carbon emission data and carbon emission warning value, carbon emission warnings are issued to the target area within the warning period.

[0024] In a second aspect, the present application also provides a carbon emission early warning device, comprising:

[0025] An acquisition module is used to obtain historical energy data of the target area, including historical electricity consumption data and historical carbon emission data;

[0026] A first determination module is used to determine a historical carbon emission conversion coefficient based on historical electricity consumption data and historical carbon emission data;

[0027] The first prediction module is used to predict the target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient;

[0028] The second determination module is used to determine the target carbon emission data according to the target carbon emission conversion coefficient;

[0029] The early warning module is used to issue carbon emission warnings to the target area based on the target carbon emission data and the carbon emission warning value.

[0030] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the carbon emission warning method described in any one of the first aspects above.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the carbon emission early warning method described in any one of the first aspects above.

[0032] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the carbon emission warning method described in any one of the first aspects above.

[0033] The above-mentioned carbon emission early warning method, device, equipment, storage medium and program product first obtains the historical energy data of the target area, including historical electricity consumption data and historical carbon emission data. Then, based on the historical electricity consumption data and historical carbon emission data, the historical carbon emission conversion coefficient can be determined. Then, the target carbon emission conversion coefficient is predicted based on the historical carbon emission conversion coefficient. The target carbon emission data is further determined based on the target carbon emission conversion coefficient. Finally, a carbon emission early warning is performed on the target area based on the target carbon emission data and the carbon emission early warning value. In this way, the target carbon emission data of the target area for the future time period can be predicted based on the historical energy data. Then, a carbon emission early warning is performed on the target area based on the target carbon emission data and the set carbon emission early warning value, thereby achieving quantitative prediction and early warning of carbon emissions, and the accuracy of carbon emission early warning is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A schematic diagram of a process for carbon emission early warning method in one embodiment;

[0036] Figure 2 A schematic flow chart of a carbon emission early warning method in another embodiment;

[0037] Figure 3 A schematic flow chart of a carbon emission early warning method in another embodiment;

[0038] Figure 4 A schematic flow chart of a carbon emission early warning method in another embodiment;

[0039] Figure 5 A schematic flow chart of a carbon emission early warning method in another embodiment;

[0040] Figure 6 This is a structural block diagram of a carbon emission early warning device in one embodiment;

[0041] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] In an exemplary embodiment, Figure 1 As shown, a carbon emission early warning method is provided. This method is described using a server as an example. The server can be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing a cloud computing server. It is understood that the method can also be applied to a terminal, or to a system including a terminal and a server, and is implemented through interaction between the terminal and the server. The method includes the following steps:

[0044] Step 101: Acquire historical energy data of a target area.

[0045] Historical energy data includes historical electricity consumption data and historical carbon emissions data. Historical energy data is time series data. Historical electricity consumption data is a sequence of electricity consumption data over a period of history, and historical carbon emissions data is a sequence of carbon emissions data over a period of history. Historical energy data may also include multi-dimensional information data such as energy consumption data and product output data. If there are anomalies in historical electricity consumption data and historical carbon emissions data, these multi-dimensional information data can be used to supplement and correct these data.

[0046] Alternatively, historical electricity consumption data can be obtained through official channels such as the "Energy Statistical Yearbook" and the "Electric Power Industry Statistical Data Compilation," which provide annual electricity consumption data by province and industry. Some provinces also publicly disclose monthly electricity consumption data through monthly statistical reports. Historical carbon emissions data can be partially obtained for key enterprises through environmental information platforms, while for other enterprises, it can be obtained through carbon emission datasets published by other research institutions. After obtaining historical electricity consumption and carbon emissions data, these data should be cleaned and preprocessed, including correcting missing and erroneous data.

[0047] Because energy consumption and product output data are relatively continuous, abnormal data can be supplemented and corrected using multi-dimensional information such as energy consumption and product output data. Energy consumption and product output data can be obtained through the following methods. For example, the "Energy Statistical Yearbook" and local monthly statistical reports include energy consumption data by industry. Industry associations such as the Iron and Steel Industry Association regularly release data on the output of key industrial products, providing basic support for industry-level carbon emissions calculations.

[0048] Step 102: Determine a historical carbon emission conversion coefficient based on historical electricity consumption data and historical carbon emission data.

[0049] The historical carbon emissions conversion factor, also known as the electricity-to-carbon conversion factor, is the ratio of carbon emissions from energy activities to electricity consumption. It represents the average carbon emissions from energy activities per unit of electricity consumption. The calculation formula is as follows.

[0050]

[0051] in, For the period Historical carbon emission conversion factor within; For the period The total carbon emissions from energy activities within the period, i.e., the carbon emissions data within period t in the historical carbon emissions data; For the period The total electricity consumption within the period t, that is, the electricity consumption data within the period t in the historical electricity consumption data.

[0052] Step 103: predicting the target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient.

[0053] The target carbon emission conversion coefficient is the carbon emission conversion coefficient for the future period. This coefficient will change over time due to factors such as technological updates, energy structure transformation, and low-carbon social development. Therefore, after calculating the historical time series distribution of the carbon emission conversion coefficient based on historical electricity data and historical carbon emission data, we can use time series analysis to predict the carbon emission conversion coefficient for the historical period to obtain the target carbon emission conversion coefficient for the future period.

[0054] Optionally, an autoregressive moving average model can be used to predict the carbon emission conversion coefficient. The calculation formula is as follows.

[0055]

[0056] in, is the model autoregressive parameter; is the model sliding average parameter; is the autoregressive order of the model; is the sliding average order of the model; For the period of white noise.

[0057] Step 104: Determine target carbon emission data based on the target carbon emission conversion coefficient.

[0058] The target carbon emission data refers to the carbon emission data for energy activities in the future period. After the target carbon emission conversion coefficient for the future period is predicted, the target carbon emission data can be calculated based on the target carbon emission data and the electricity consumption data for the future period. Optionally, the electricity consumption data for the future period can be determined based on the electricity consumption plan for the target area or predicted based on the historical electricity consumption data for the target area, which is not limited in this embodiment of the present application.

[0059] Step 105: Provide a carbon emission warning for the target area based on the target carbon emission data and the carbon emission warning value.

[0060] Based on the target carbon emissions data and carbon emission warning value for the target area in the future period, it is determined whether the target carbon emissions data meets the carbon emission warning value conditions, and a carbon emission warning is issued for the target area. The carbon emission warning value can be determined based on the current emission reduction goals and needs of the area, and multiple carbon emission warning values ​​can be included to provide different levels of warning.

[0061] In this embodiment, first, the historical energy data of the target area, including historical electricity consumption data and historical carbon emission data, is obtained. Then, based on the historical electricity consumption data and historical carbon emission data, the historical carbon emission conversion coefficient can be determined. Next, the target carbon emission conversion coefficient is predicted based on the historical carbon emission conversion coefficient. The target carbon emission data is further determined based on the target carbon emission conversion coefficient. Finally, a carbon emission warning is performed for the target area based on the target carbon emission data and the carbon emission warning value. In this way, the target carbon emission data for the future time period of the target area can be predicted based on the historical energy data. Then, a carbon emission warning is performed for the target area based on the target carbon emission data and the set carbon emission warning value, thereby achieving quantitative prediction and warning of carbon emissions, and the accuracy of the carbon emission warning is higher.

[0062] In one embodiment, before determining the historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data, the method also includes: if the time frequencies of the historical electricity consumption data and the historical carbon emission data are inconsistent, performing frequency conversion processing on the historical electricity consumption data and the historical carbon emission data.

[0063] Historical electricity consumption data and historical carbon emissions data are time series data. However, due to different data sources, the time series frequencies may be inconsistent. For example, historical electricity consumption data is time series data with daily units, while historical carbon emissions data is time series data with monthly units. Therefore, to unify the time frequencies, the Chow-Lin method can be used to transform the data frequencies of historical electricity consumption data and historical carbon emissions data. The calculation formula is shown below.

[0064]

[0065] in, is a known low-frequency data sequence; For relevant high-frequency data explanatory variables; is the conversion factor; is a random disturbance term.

[0066] In an optional embodiment of the present application, Figure 2 As shown, before the above step 102 of predicting and obtaining the target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient, the method further includes:

[0067] Step 201: Perform a stability test on the historical carbon emission conversion coefficient.

[0068] For time series, stationarity testing is a prerequisite for forecasting them. Therefore, before forecasting the historical carbon emission conversion coefficient to obtain the target carbon emission conversion coefficient, it is necessary to first conduct a stationarity test on the historical carbon emission conversion coefficient. This can be done using the unit root test. The test formula is shown below.

[0069]

[0070] in, is the original time series, that is, the time series that needs to be tested for stationarity; is the difference symbol; is a constant term; is the time trend term; is the lag term coefficient; is a white noise sequence; is the lag order; Lag The difference lag term The coefficient of .

[0071] Step 202: When the historical carbon emission conversion coefficient is a non-stationary sequence, a stationary transformation process is performed on the historical carbon emission conversion coefficient.

[0072] After the above-mentioned stationarity test, if the time series, such as the historical carbon emission conversion coefficient series, is a non-stationary series, it is necessary to perform stationarity change processing, including differential, logarithmic and other transformation processing, and then perform the stationarity test again until the requirements are met.

[0073] Next, we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) to determine the order of the autoregressive moving average model. The order determination formula is shown below.

[0074]

[0075]

[0076] in, is the likelihood function; is the model order; For the model in order AIC value under ; For the model in order BIC value under ; is the highest order of model fitting; is the sequence width.

[0077] According to the determined optimal order of the autoregressive moving average model, the historical carbon emission conversion coefficient is predicted to obtain the target carbon emission conversion coefficient.

[0078] In the embodiment of the present application, after determining the target carbon emission conversion coefficient, the target carbon emission data for the future period can be determined according to the target carbon emission conversion coefficient and the electricity consumption data for the future period. If the electricity consumption data for the future period cannot be obtained through data, such as Figure 3 As shown, according to the target carbon emission conversion coefficient, the target carbon emission data is determined, including:

[0079] Step 301 : Predict target power consumption data based on historical power consumption data.

[0080] If it is not possible to obtain the target electricity consumption data for the future period through the electricity consumption data of the target area, the target electricity consumption data can be predicted based on the historical electricity consumption data using the same prediction method as above. Specifically, a high-frequency or low-frequency time series, i.e., historical electricity consumption data, is first constructed based on the original electricity consumption data of the region / industry / enterprise by time period. If there is inconsistency in time frequency, the high-frequency electricity consumption data is down-converted or reversely up-converted to supplement the missing data using the Chow-Lin method. The unit root test method is then used to test the stationarity of the electricity consumption series. Non-stationary series are made stationary through difference or logarithmic transformation. The optimal order (p, d, q) of the autoregressive moving average model is determined based on the AIC and BIC criteria. Finally, a rolling forecast of the historical electricity consumption data is performed using the fitted autoregressive moving average model.

[0081] Step 302: Determine target carbon emission data based on the target carbon emission conversion coefficient and target electricity consumption data.

[0082] After obtaining the target carbon emission conversion coefficient and target electricity consumption data for the future period, the target carbon emission data for the future period can be calculated.

[0083] Optionally, the target carbon emission data is determined as the linear product of the target carbon emission conversion coefficient and the target electricity consumption data. As shown in the following formula, Forecast sequence of target carbon emission conversion coefficients for future periods (e.g. hourly / daily / monthly level), is the target electricity consumption data for the corresponding period.

[0084]

[0085] in, For the corresponding period The predicted carbon emission value, namely the target carbon emission data, is used to characterize the dynamic evolution trend of carbon emissions from energy activities.

[0086] In an exemplary embodiment, according to the target carbon emission data and the carbon emission warning value, a carbon emission warning is performed on the target area, such as Figure 4 Shown, including:

[0087] Step 401 : determining a carbon emission warning value within a warning period based on historical carbon emission data and a maximum emission threshold of a target period.

[0088] The target period is the period for which carbon emissions forecasting is required. It can be set on a daily, weekly, or monthly basis. The maximum emission threshold for the target period can be calculated based on the annual carbon emission cap for the target area. The annual carbon emission cap for the target area can be determined based on the emission reduction goals and requirements of various entities, including regional management and regulatory authorities, enterprises, and third-party organizations. The maximum emission threshold for the target period can be calculated using the following formula.

[0089]

[0090] in, is the periodic coefficient, Indicates that the warning period has expired The cumulative actual carbon emissions before the current period. When no actual carbon emissions are generated, this value is equal to 0; Indicates the number of remaining cycles in a year; The annual carbon emission cap for the target area; Economic sensitivity (default is 0.1, meaning the threshold increases or decreases by 1% for every 10% increase or decrease in electricity consumption growth); Indicates the growth rate of electricity consumption data in this cycle.

[0091] The cycle coefficient can be calculated according to the following formula: using the historical carbon emission data of the corresponding cycle in the past few years (for example, the past three years), calculate the current cycle in the historical data Compared with the average value of all periods in the year proportion.

[0092]

[0093] Among them, after deducting the cumulative value of historical carbon emissions before the current cycle from the total annual carbon emissions budget, the remaining carbon emissions quota is obtained, and the remaining carbon emissions quota is evenly distributed according to the remaining cycles. Therefore, as time goes by, the carbon emissions quotas of subsequent cycles will be dynamically adjusted based on the accumulated actual carbon emissions. If the actual carbon emissions in a certain cycle exceed the initial quota, the subsequent carbon emissions quota will be automatically tightened; otherwise, it will be relaxed accordingly to ensure that the total annual carbon emissions are effectively controlled. If the electricity consumption data changes due to policy regulation or rapid economic development, the carbon emission warning value will also be dynamically revised to ensure that the warning model has the ability to adapt to economic fluctuations.

[0094] Step 402: Based on the target carbon emission data and the carbon emission warning value, a carbon emission warning is issued to the target area within the warning period.

[0095] Target carbon emission data obtained based on the above calculation By comparing with the carbon emission warning value, a risk assessment indicator system is constructed to issue carbon emission warnings for the target area within the warning period. The specific triggering conditions are shown in Table 1 below.

[0096] Table 1 Correspondence table of risk assessment indicator system

[0097]

[0098] In this application's examples, historical carbon emissions data is analyzed to extract the true contribution of each period to the annual total, serving as the basis for initial budget allocation. This fully accounts for the impact of seasonal variations and production rhythms, ensuring that budget allocations more closely align with actual emission patterns, avoiding biases caused by average allocations and improving the accuracy and practicality of early warnings.

[0099] Furthermore, compared to traditional static budgets, by introducing a dynamic correction mechanism for the remaining budget, the budget for subsequent cycles is adjusted in real time in combination with historical weights. When carbon emissions exceed the standard or are lower than expected at a certain stage, the system automatically tightens or relaxes the subsequent budget to ensure that the annual total carbon emissions target is controllable and improve the flexibility and robustness of carbon emissions management throughout the entire process. At the same time, the annual carbon emissions budget can be flexibly broken down into monthly, daily, and even hourly levels to meet the refined early warning needs of different entities at different management levels. Through high-time-resolution disassembly and dynamic adjustment mechanisms, full-process coverage from macro-target control to micro-real-time monitoring can be achieved, improving the accuracy and response efficiency of carbon emissions early warnings.

[0100] In the embodiments of the present application, Figure 5 As shown, a carbon emission early warning method is provided, including:

[0101] Step 501: Acquire historical energy data of a target area.

[0102] Step 502: Determine a historical carbon emission conversion coefficient based on historical electricity consumption data and historical carbon emission data.

[0103] Step 503: predicting based on the historical carbon emission conversion coefficient to obtain the target carbon emission conversion coefficient.

[0104] Step 504 : Predicting the target power consumption data based on the historical power consumption data.

[0105] Step 505 : Determine target carbon emission data according to the target carbon emission conversion coefficient and the target electricity consumption data.

[0106] Step 506: Determine the carbon emission warning value within the warning period based on the historical carbon emission data and the maximum emission threshold of the target period.

[0107] Step 507: Based on the target carbon emission data and the carbon emission warning value, a carbon emission warning is issued to the target area within the warning period.

[0108] This implementation provides scientific and accurate carbon emissions warning signals to multiple entities by quantifying forecast uncertainty and setting warning thresholds. When forecasts indicate that carbon emissions may exceed pre-set safety limits, the system automatically triggers an alert, prompting relevant departments or businesses to take timely countermeasures, thereby mitigating potential risks and promoting the achievement of carbon reduction targets.

[0109] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0110] Based on the same inventive concept, embodiments of the present application also provide a carbon emission warning device for implementing the aforementioned carbon emission warning method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more carbon emission warning device embodiments provided below can be found in the above-described limitations of the carbon emission warning method and will not be further elaborated here.

[0111] In an exemplary embodiment, Figure 6 As shown, a carbon emission early warning device 600 is provided, comprising: an acquisition module 601, a first determination module 602, a first prediction module 603, a second determination module 604 and an early warning module 605, wherein:

[0112] An acquisition module 601 is used to acquire historical energy data of a target area, where the historical energy data includes historical electricity consumption data and historical carbon emission data;

[0113] A first determining module 602 is configured to determine a historical carbon emission conversion coefficient based on historical electricity consumption data and historical carbon emission data;

[0114] The first prediction module 603 is used to predict and obtain a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient;

[0115] The second determination module 604 is configured to determine target carbon emission data according to the target carbon emission conversion coefficient;

[0116] The early warning module 605 is used to issue a carbon emission early warning to the target area based on the target carbon emission data and the carbon emission early warning value.

[0117] In one embodiment, the second determination module 604 is specifically configured to predict and obtain target power consumption data based on historical power consumption data; and determine target carbon emission data based on the target carbon emission conversion coefficient and the target power consumption data.

[0118] In one embodiment, the device further includes a frequency conversion module for performing frequency conversion processing on the historical power consumption data and the historical carbon emission data if the time frequencies of the historical power consumption data and the historical carbon emission data are inconsistent.

[0119] In one embodiment, the device further includes a stationarity test module for performing a stationarity test on the historical carbon emission conversion coefficient; when the historical carbon emission conversion coefficient is a non-stationary sequence, the historical carbon emission conversion coefficient is subjected to a stationarity transformation process.

[0120] In one embodiment, the second determining module 604 is specifically configured to determine the target carbon emission data as a linear product of the target carbon emission conversion coefficient and the target electricity consumption data.

[0121] In one embodiment, the warning module 605 is specifically used to determine the carbon emission warning value within the warning period based on historical carbon emission data and the maximum emission threshold of the target period; and to issue a carbon emission warning to the target area within the warning period based on the target carbon emission data and the carbon emission warning value.

[0122] Each module in the aforementioned carbon emission early warning device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a computer device's memory in the form of software, allowing the processor to call and execute the corresponding operations of each module.

[0123] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store historical energy data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a carbon emission early warning method is implemented.

[0124] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0125] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining historical energy data of a target area, wherein the historical energy data includes historical electricity consumption data and historical carbon emission data; determining a historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data; predicting a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient; determining target carbon emission data based on the target carbon emission conversion coefficient; and issuing a carbon emission warning for the target area based on the target carbon emission data and the carbon emission warning value.

[0126] In one embodiment, when the processor executes the computer program, it further implements the following steps: predicting based on historical power consumption data to obtain target power consumption data; and determining target carbon emission data based on the target carbon emission conversion coefficient and the target power consumption data.

[0127] In one embodiment, when the processor executes the computer program, the following steps are further implemented: if the time frequencies of the historical power consumption data and the historical carbon emission data are inconsistent, frequency conversion processing is performed on the historical power consumption data and the historical carbon emission data.

[0128] In one embodiment, when the processor executes the computer program, it further implements the following steps: performing a stationary test on the historical carbon emission conversion coefficient; and performing a stationary transformation on the historical carbon emission conversion coefficient when the historical carbon emission conversion coefficient is a non-stationary sequence.

[0129] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: determining the target carbon emission data as the target carbon emission data by linearly multiplying the target carbon emission conversion coefficient and the target electricity consumption data.

[0130] In one embodiment, when the processor executes the computer program, it also implements the following steps: determining the carbon emission warning value within the warning period based on historical carbon emission data and the maximum emission threshold of the target period; and issuing a carbon emission warning for the target area within the warning period based on the target carbon emission data and the carbon emission warning value.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: obtaining historical energy data of a target area, the historical energy data including historical electricity consumption data and historical carbon emission data; determining a historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data; predicting a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient; determining target carbon emission data based on the target carbon emission conversion coefficient; and issuing a carbon emission warning for the target area based on the target carbon emission data and the carbon emission warning value.

[0132] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining target power consumption data by prediction based on historical power consumption data; and determining target carbon emission data based on the target carbon emission conversion coefficient and the target power consumption data.

[0133] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: if the time frequencies of the historical power consumption data and the historical carbon emission data are inconsistent, frequency conversion processing is performed on the historical power consumption data and the historical carbon emission data.

[0134] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: performing a stationarity test on the historical carbon emission conversion coefficient; and performing a stationarity transformation on the historical carbon emission conversion coefficient when the historical carbon emission conversion coefficient is a non-stationary sequence.

[0135] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: a linear product of the target carbon emission conversion coefficient and the target electricity consumption data is determined as the target carbon emission data.

[0136] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the carbon emission warning value within the warning period based on historical carbon emission data and the maximum emission threshold of the target period; and issuing a carbon emission warning for the target area within the warning period based on the target carbon emission data and the carbon emission warning value.

[0137] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: obtaining historical energy data of a target area, the historical energy data including historical electricity consumption data and historical carbon emission data; determining a historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data; predicting a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient; determining target carbon emission data based on the target carbon emission conversion coefficient; and issuing a carbon emission warning for the target area based on the target carbon emission data and the carbon emission warning value.

[0138] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: obtaining target power consumption data by prediction based on historical power consumption data; and determining target carbon emission data based on the target carbon emission conversion coefficient and the target power consumption data.

[0139] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: if the time frequencies of the historical power consumption data and the historical carbon emission data are inconsistent, frequency conversion processing is performed on the historical power consumption data and the historical carbon emission data.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: performing a stationarity test on the historical carbon emission conversion coefficient; and performing a stationarity transformation on the historical carbon emission conversion coefficient when the historical carbon emission conversion coefficient is a non-stationary sequence.

[0141] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: a linear product of the target carbon emission conversion coefficient and the target electricity consumption data is determined as the target carbon emission data.

[0142] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the carbon emission warning value within the warning period based on historical carbon emission data and the maximum emission threshold of the target period; and issuing a carbon emission warning for the target area within the warning period based on the target carbon emission data and the carbon emission warning value.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0144] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0145] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A carbon emission early warning method, characterized in that: The method comprises: Acquiring historical energy data of a target area, wherein the historical energy data includes historical electricity consumption data and historical carbon emission data; Determining a historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data; Predicting a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient; Determining target carbon emission data according to the target carbon emission conversion coefficient; A carbon emission warning is performed on the target area according to the target carbon emission data and the carbon emission warning value.

2. The method according to claim 1, characterized in that Determining target carbon emission data according to the target carbon emission conversion coefficient includes: Predicting target power consumption data based on the historical power consumption data; The target carbon emission data is determined according to the target carbon emission conversion coefficient and the target electricity consumption data.

3. The method according to claim 1, characterized in that Before determining the historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data, the method further includes: If the time frequencies of the historical power consumption data and the historical carbon emission data are inconsistent, frequency conversion processing is performed on the historical power consumption data and the historical carbon emission data.

4. The method according to claim 1, wherein Before predicting and obtaining the target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient, the method further includes: Conducting a stability test on the historical carbon emission conversion coefficient; In the case that the historical carbon emission conversion coefficient is a non-stationary sequence, a stationary transformation process is performed on the historical carbon emission conversion coefficient.

5. The method according to claim 2, characterized in that The determining the target carbon emission data according to the target carbon emission conversion coefficient and the target electricity consumption data includes: The target carbon emission data is determined as the target carbon emission data by multiplying the target carbon emission conversion coefficient by a linear product of the target electricity consumption data.

6. The method according to claim 1, characterized in that The step of providing a carbon emission warning for the target area based on the target carbon emission data and the carbon emission warning value includes: Determining the carbon emission warning value within the warning period based on the historical carbon emission data and the maximum emission threshold of the target period; According to the target carbon emission data and the carbon emission warning value, a carbon emission warning is performed on the target area within the warning period.

7. A carbon emission warning device, characterized in that: The device comprises: An acquisition module is used to acquire historical energy data of a target area, wherein the historical energy data includes historical electricity consumption data and historical carbon emission data; A first determining module is configured to determine a historical carbon emission conversion coefficient based on the historical electricity consumption data and the historical carbon emission data; A first prediction module is used to predict and obtain a target carbon emission conversion coefficient based on the historical carbon emission conversion coefficient; A second determination module is configured to determine target carbon emission data according to the target carbon emission conversion coefficient; The early warning module is used to issue a carbon emission early warning to the target area based on the target carbon emission data and the carbon emission early warning value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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