Carbon emission monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to a carbon emission monitoring method and device, computer equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a plurality of pieces of original energy consumption time sequence data of a to-be-monitored place; acquiring the monitoring frequency of the to-be-monitored place according to the plurality of pieces of original energy consumption time sequence data; according to a preset standard time sequence starting point and the monitoring frequency, performing alignment processing on original timestamps in the multiple pieces of original energy use time sequence data, and correspondingly obtaining multiple pieces of aligned energy use time sequence data; and determining a carbon emission monitoring result of the to-be-monitored place based on the plurality of pieces of aligned energy use time sequence data. By adopting the method, the accuracy of a carbon emission monitoring result can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a carbon emission monitoring method, device, computer equipment and storage medium. Background Art

[0002] With the increasing attention paid to environmental protection, the monitoring and management of carbon emissions have also become a focus of attention.

[0003] Taking the carbon emission monitoring of enterprises as an example, in the relevant technologies, major enterprises can use energy management platforms to calculate the carbon emissions of the collected energy consumption data of enterprises, so as to carry out management operations such as monitoring and optimizing the carbon emissions situation within the enterprise.

[0004] However, the energy consumption data collected from enterprises in related technologies have data quality issues, which leads to deviations in the calculation of carbon emissions and affects the carbon emission monitoring of enterprises. Summary of the invention

[0005] Based on this, it is necessary to provide a carbon emission monitoring method, device, computer equipment and storage medium to address the above technical problems, which can improve the accuracy of carbon emission monitoring results.

[0006] In a first aspect, the present application provides a carbon emission monitoring method, the method comprising:

[0007] Obtain multiple pieces of original energy consumption time series data of the place to be monitored;

[0008] Acquire the monitoring frequency of the place to be monitored according to the plurality of original energy consumption time series data;

[0009] According to a preset standard time series starting point and the monitoring frequency, the original timestamps in the multiple pieces of original energy consumption time series data are aligned, and a plurality of aligned energy consumption time series data are correspondingly obtained;

[0010] The carbon emission monitoring result of the place to be monitored is determined based on the multiple aligned energy consumption time series data.

[0011] In one embodiment, obtaining the monitoring frequency of the place to be monitored according to the plurality of original energy consumption time series data includes:

[0012] For any piece of original energy consumption time series data, determine the data collection frequency of the original energy consumption time series data according to the difference between at least two adjacent original timestamps in the original energy consumption time series data;

[0013] The monitoring frequency is determined according to the data collection frequency of each of the original energy consumption time series data.

[0014] In one embodiment, determining the monitoring frequency according to the data collection frequency of each of the original energy consumption time series data includes:

[0015] Obtaining the least common multiple of the data collection frequencies of the original energy consumption time series data;

[0016] The monitoring frequency is determined according to a preset frequency coefficient and the least common multiple.

[0017] In one embodiment, the aligning of original timestamps in the plurality of original energy usage time series data according to a preset standard time series starting point and the monitoring frequency, and correspondingly obtaining a plurality of aligned energy usage time series data, includes:

[0018] Taking the starting point of the standard time sequence as the first standard timestamp of the standard time sequence and taking the monitoring frequency as the time interval, obtaining a standard time sequence including a plurality of standard timestamps;

[0019] According to the standard time series and the monitoring frequency, the original timestamps in the multiple pieces of original energy usage time series data are aligned, and the multiple pieces of aligned energy usage time series data are correspondingly obtained.

[0020] In one embodiment, the aligning of original timestamps in the plurality of original energy usage time series data according to the standard time series and the monitoring frequency, and correspondingly obtaining the plurality of aligned energy usage time series data, includes:

[0021] Taking the monitoring frequency as the length of the data alignment window, respectively determining the data alignment window of each of the standard timestamps;

[0022] For any piece of original energy usage time series data, obtaining an aligned energy usage value in each of the data alignment windows in the original energy usage time series data;

[0023] The time series data composed of the alignment energy values ​​in each of the data alignment windows is determined as the alignment energy time series data corresponding to the original energy time series data.

[0024] In one embodiment, for any piece of original energy usage time series data, obtaining the aligned energy usage value in each of the data alignment windows in the original energy usage time series data includes:

[0025] For any data alignment window, obtaining the time distance between the original timestamp in the original energy usage time series data that falls within the data alignment window and the standard timestamp of the data alignment window;

[0026] Get the target original timestamp corresponding to the minimum time distance;

[0027] The original energy consumption value corresponding to the target original timestamp is determined as the aligned energy consumption value of the original energy consumption time series data within the data alignment window.

[0028] In one embodiment, the method further comprises:

[0029] Determine a target standard timestamp, where the target standard timestamp is a standard timestamp whose number of original timestamps falling within the corresponding data alignment window is zero and / or a standard timestamp whose corresponding target original timestamp does not have an original energy consumption value;

[0030] Determine two candidate original energy usage values ​​whose original timestamps are adjacent to the target standard timestamp;

[0031] An aligned energy usage value of the target standard timestamp is determined based on the two candidate original energy usage values.

[0032] In one embodiment, determining the carbon emission monitoring result of the site to be monitored based on the plurality of aligned energy usage time series data includes:

[0033] Identify the abnormal data in the plurality of alignment energy usage time series data respectively, and obtain the abnormal energy usage value in each alignment energy usage time series data;

[0034] Obtaining a target standard timestamp of each of the abnormal energy consumption values ​​in a standard time sequence, wherein the standard time sequence is determined according to the standard time sequence starting point and the monitoring frequency;

[0035] For each piece of original energy consumption time series data, determine two candidate original energy consumption values ​​whose original timestamps are adjacent to the target standard timestamp;

[0036] The abnormal energy consumption value is replaced according to the two candidate original energy consumption values ​​to obtain a plurality of standard energy consumption time series data;

[0037] The carbon emission monitoring result is determined based on the multiple standard energy consumption time series data.

[0038] In one embodiment, the step of respectively identifying the abnormal data in the plurality of alignment energy usage time series data and obtaining the abnormal energy usage value in each alignment energy usage time series data includes:

[0039] Determine a first quartile, a second quartile, and an interquartile range of the plurality of aligned energy usage time series data;

[0040] Determine an energy usage threshold range for each piece of the aligned energy usage time series data according to the first quartile, the second quartile and the interquartile range of each piece of the aligned energy usage time series data;

[0041] In each piece of the alignment energy usage time series data, an alignment energy usage value exceeding a corresponding energy usage threshold range is determined as the abnormal energy usage value.

[0042] In one embodiment, the replacing process of the abnormal energy consumption value according to the two candidate original energy consumption values ​​to obtain multiple standard energy consumption time series data includes:

[0043] Performing linear interpolation on the two candidate original energy consumption values ​​to obtain a corrected energy consumption value of each abnormal energy consumption value;

[0044] The abnormal energy consumption value is replaced according to the corrected energy consumption value to obtain the plurality of standard energy consumption time series data.

[0045] In one embodiment, determining the carbon emission monitoring result of the site to be monitored based on the plurality of aligned energy usage time series data includes:

[0046] Based on the multiple aligned energy usage time series data and the carbon emission factors of the multiple aligned energy usage time series data, the carbon emission monitoring result of the place to be monitored is determined.

[0047] In one embodiment, the plurality of aligned energy usage time series data include first aligned energy usage time series data of a first energy type and second aligned energy usage time series data of a second energy type, and determining the carbon emission monitoring result of the to-be-monitored place based on the plurality of aligned energy usage time series data and the carbon emission factor of the plurality of aligned energy usage time series data includes:

[0048] Determining a first carbon emission monitoring result of the place to be monitored according to the first aligned energy consumption time series data and the first carbon emission factor;

[0049] Determine a second carbon emission monitoring result of the place to be monitored according to the second aligned energy consumption time series data and the second carbon emission factor;

[0050] The total carbon emission monitoring result of the place to be monitored is determined according to the first carbon emission monitoring result and the second carbon emission monitoring result.

[0051] In a second aspect, an embodiment of the present application provides a carbon emission monitoring device, comprising:

[0052] An acquisition module is used to acquire multiple pieces of original energy consumption time series data in the place to be monitored;

[0053] An alignment module, used to obtain the monitoring frequency of the place to be monitored according to the multiple original energy consumption time series data, and align the original timestamps in the multiple original energy consumption time series data according to a preset standard time series starting point and the monitoring frequency, and correspondingly obtain multiple aligned energy consumption time series data;

[0054] A determination module is used to determine the carbon emission monitoring result of the place to be monitored based on the multiple aligned energy consumption time series data.

[0055] In a third aspect, an embodiment of the present application provides a carbon emission monitoring system, including:

[0056] A plurality of energy collection devices, each of which is arranged at a location to be monitored and is used to collect raw energy usage time series data;

[0057] Like the aforementioned carbon emission monitoring device, the carbon emission monitoring device is connected to the multiple energy collection devices respectively.

[0058] In a fourth aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the aforementioned method when executing the computer program.

[0059] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the aforementioned method when executed by a processor.

[0060] The above-mentioned carbon emission monitoring method, device, computer equipment and storage medium obtain multiple original energy consumption time series data of the place to be monitored, and obtain the monitoring frequency of the place to be monitored based on the multiple original energy consumption time series data, and align the original timestamps in the multiple original energy consumption time series data according to the preset standard time series starting point and monitoring frequency, and obtain multiple aligned energy consumption time series data accordingly, so that the carbon emission monitoring result of the place to be monitored can be determined based on the multiple aligned energy consumption time series data. Since the aligned energy consumption time series data uses the standard time series starting point as the first standard timestamp, and the original timestamp of each original energy consumption time series data is aligned at a uniform monitoring frequency, the standard timestamp of each aligned energy consumption time series data obtained after alignment corresponds to the corresponding The same, and the interval between any two adjacent standard timestamps in each data is equal to the monitoring frequency. Therefore, the uniformity of each aligned energy consumption time series data in the time dimension is achieved, which solves the problem of erroneous carbon emission monitoring results caused by low data quality during data sampling, such as inconsistent data collection time, reduces errors and uncertainties in the data collection process, and improves the accuracy and credibility of carbon emission monitoring results of the monitored sites. Since the frequencies of multiple aligned energy consumption time series data are unified as the monitoring frequency, the problems of difficulty in data aggregation and inaccurate aggregation results caused by inconsistent sampling frequencies of multiple energy types are also solved. While ensuring real-time performance, the computing resource consumption of time series data processing is reduced, which can effectively improve the efficiency of carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 An application environment diagram of a carbon emission monitoring method provided by an embodiment;

[0062] Figure 2 A schematic diagram of a process for monitoring carbon emissions provided by an embodiment;

[0063] Figure 3 A schematic diagram of a process of step S202 in a carbon emission monitoring method provided in an embodiment;

[0064] Figure 4 A schematic diagram of the process of step S302 in a carbon emission monitoring method provided in an embodiment;

[0065] Figure 5 A schematic diagram of the process of step S203 in a carbon emission monitoring method provided in an embodiment;

[0066] Figure 6 A schematic diagram of the process of step S502 in a carbon emission monitoring method provided in an embodiment;

[0067] Figure 7 A schematic diagram of the process of step S602 in a carbon emission monitoring method provided in an embodiment;

[0068] Figure 8 A schematic flow chart of step S204 in a carbon emission monitoring method provided in an embodiment;

[0069] Fig. 9 A schematic diagram of a process of step S901 in a carbon emission monitoring method provided in an embodiment;

[0070] Fig.10 A schematic diagram of a box plot algorithm provided by one embodiment;

[0071] Fig.11 A schematic diagram of the process of step S804 in a carbon emission monitoring method provided in an embodiment;

[0072] Fig.12 A schematic flow chart of a carbon emission monitoring method provided in another embodiment;

[0073] Fig.13 A schematic flow chart of a carbon emission monitoring method provided in yet another embodiment;

[0074] Fig.14 An original timestamp sequence of 5 original energy usage time series data provided for an embodiment;

[0075] Fig.15 A schematic diagram of the relationship between an original timestamp of an original energy consumption time series data and a data alignment window provided in an embodiment;

[0076] Fig.16 A schematic diagram of box plot analysis results of 5 aligned energy usage time series data provided by an embodiment;

[0077] Fig.17 A schematic diagram of abnormal data and missing data of an alignment energy usage time series data provided in an embodiment;

[0078] Fig.18 An embodiment provides a pair Fig.17 A schematic diagram of standard energy consumption time series data after replacing abnormal data and missing data shown;

[0079] Fig.19 A structural block diagram of a carbon emission monitoring device provided by an embodiment;

[0080] Fig. 20 A structural block diagram of a carbon emission monitoring system provided by an embodiment;

[0081] Fig.21 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0083] The carbon emission monitoring method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can obtain multiple original energy consumption time series data of the place to be monitored, and then obtain the monitoring frequency of the place to be monitored based on the multiple original energy consumption time series data, and align the original timestamps in the multiple original energy consumption time series data according to the preset standard time series starting point and monitoring frequency, and obtain multiple aligned energy consumption time series data accordingly, so as to determine the carbon emission monitoring results of the place to be monitored based on the multiple aligned energy consumption time series data. Among them, the terminal 102 can be but is not limited to various computers, laptops, tablet computers and Internet of Things devices. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0084] In one embodiment, Figure 2 As shown, a carbon emission monitoring method is provided, which is applied to Figure 1The terminal is taken as a computer for example, and the method includes the following steps S201 to S204.

[0085] S201: Obtain multiple pieces of original energy consumption time series data of the place to be monitored.

[0086] The places to be monitored refer to places where there is a need for carbon emission monitoring, including but not limited to enterprises, shopping malls, residential areas, factories, etc. Each piece of original energy consumption time series data includes multiple original energy consumption data arranged in chronological order. Each original energy consumption data includes an original energy consumption value and an original timestamp. The original energy consumption value is used to indicate the cumulative consumption of a certain energy in the local area of ​​the place to be monitored at the time marked by the original timestamp. The original timestamp is used to indicate the time point when the original energy consumption value was collected. The energy types recorded in different original energy consumption time series data can be the same or different. For example, a piece of original energy consumption time series data is used to represent the cumulative electricity consumption of household A in the monitored community within one month; for example, the original energy consumption time series data includes the original energy consumption data {00:02, 259.4} and {00:12, 261.2}, wherein the original energy consumption data {00:02, 259.4} represents that the cumulative electricity consumption of household A at time 00:02 is 259.4, its original timestamp is 00:02, and the original energy consumption value is 259.4; the original energy consumption data {00:12, 261.2} represents that the cumulative electricity consumption of household A at time 00:12 is 261.2, its original timestamp is 00:12, and the original energy consumption value is 261.2. For another example, another piece of original energy consumption time series data is used to represent the cumulative gas consumption of household B in the monitored community within one month. The embodiments of the present application do not limit the number of original energy consumption time series data, the number of data points included in each original energy consumption time series data, the time period for monitoring the original energy consumption time series data, etc., which can be specifically determined based on the carbon emission monitoring requirements of the monitored location.

[0087] S202: Obtain the monitoring frequency of the place to be monitored according to the plurality of original energy consumption time series data, wherein the monitoring frequency is used to indicate the periodic frequency of monitoring various energy consumption conditions in the place to be monitored.

[0088] S203: According to a preset standard time series starting point and monitoring frequency, original timestamps in a plurality of original energy consumption time series data are aligned, and a plurality of aligned energy consumption time series data are correspondingly obtained.

[0089] The starting point of the standard timing is a pre-set time point. In the embodiment of the present application, the starting point of the standard timing can be understood as the starting point of the standard timing, and is used to align the starting time of the original timestamp. The standard timing refers to a group of time points arranged according to the monitoring frequency. The standard timing includes multiple standard timestamps. The standard timing uses the starting point of the standard timing as the first standard timestamp, and the time interval between each adjacent standard timestamp is equal and equal to the monitoring frequency. Among them, the number of standard timestamps included in the standard timing can be 1, 2, 3 or other suitable values, which are not limited here. The standard timestamp can be measured in natural time or set according to preset time recording rules. It can be set according to actual application requirements and is not limited here. For example, if the starting point of the standard time series is set to 00:00 and the monitoring frequency is 1 hour, the interval between any two adjacent standard timestamps is 1 hour. Therefore, the original timestamps of each original energy consumption time series data are aligned starting from 00:00. After the alignment, the first standard timestamp of each aligned energy consumption time series data is 00:00, the second standard timestamp is 1:00, the third standard timestamp is 2:00, and so on. The standard time series is 00:00, 1:00, 2:00, 3:00, etc.

[0090] The aligned energy usage time series data includes a plurality of aligned energy usage data arranged in chronological order, and the aligned energy usage data includes an aligned energy usage value and a standard timestamp. The standard time series starting point is the first standard timestamp of the aligned energy usage time series data. The standard timestamps of each aligned energy usage time series data are the same. The interval between any two adjacent standard timestamps in each aligned energy usage time series data is the same, and the interval between any two adjacent standard timestamps is equal to the monitoring frequency.

[0091] S204: Determine the carbon emission monitoring result of the monitored site based on the multiple aligned energy consumption time series data, wherein the carbon emission monitoring result is used to represent the total carbon emission of the monitored site within a certain period of time.

[0092] The above-mentioned carbon emission monitoring method obtains multiple original energy consumption time series data of the place to be monitored, and obtains the monitoring frequency of the place to be monitored based on the multiple original energy consumption time series data, and aligns the original timestamps in the multiple original energy consumption time series data according to the preset standard time series starting point and monitoring frequency, and correspondingly obtains multiple aligned energy consumption time series data, so that the carbon emission monitoring results of the place to be monitored can be determined based on the multiple aligned energy consumption time series data. Since the aligned energy consumption time series data uses the standard time series starting point as the first standard timestamp, and the original timestamp of each original energy consumption time series data is aligned with a uniform monitoring frequency, the standard timestamps of each aligned energy consumption time series data obtained after the alignment correspond to the same, and each data The interval between any two adjacent standard timestamps in the data is equal to the monitoring frequency. Therefore, the uniformity of each aligned energy consumption time series data in the time dimension is achieved, which solves the problem of erroneous carbon emission monitoring results caused by low data quality during data sampling, such as inconsistent data collection time, and reduces errors and uncertainties in the data collection process, and improves the accuracy and credibility of carbon emission monitoring results of the monitored sites. Since the frequencies of multiple aligned energy consumption time series data are unified as the monitoring frequency, the problems of difficulty in data aggregation and inaccurate aggregation results caused by inconsistent sampling frequencies of multiple energy types are also solved. While ensuring real-time performance, the computing resource consumption of time series data processing is reduced, which can effectively improve the efficiency of carbon emission management.

[0093] In one embodiment, Figure 2 As shown, step S202, obtaining the monitoring frequency of the place to be monitored according to multiple original energy consumption time series data, may include the following steps S301 and S302.

[0094] S301: for any piece of original energy consumption time series data, determine the data collection frequency of the original energy consumption time series data according to the difference between at least two adjacent original timestamps in the original energy consumption time series data.

[0095] For any piece of original energy consumption time series data, the computer can determine the data collection frequency of the original energy consumption time series data according to the difference between at least two adjacent original timestamps in the original energy consumption time series data. Therefore, the data collection frequency set F = {f 1 ,f 2 ,…,f i}, where f i Indicates the data collection frequency of the i-th original energy consumption time series data.

[0096] Exemplarily, the difference between multiple pairs of adjacent original timestamps in the original energy consumption time series data is determined, and the mode of the difference between multiple pairs of adjacent original timestamps is determined as the data sampling frequency of the original energy consumption time series data. The number of pairs of adjacent original timestamps can be 2, 3, 4 or other suitable values, which are not limited here.

[0097] The energy collection device can periodically collect energy consumption data of the monitored place. Exemplarily, the energy collection device may include a gas data sampling device, an electricity data sampling device, or other types of devices. Among them, the gas data sampling device is used to collect the gas usage of the monitored place, for example, a gas meter. The electricity data sampling device is used to collect the electricity usage of the monitored place, for example, an electricity meter. The data collection periods of different energy collection devices can be the same or different. Taking the monitored place as a residential residence as an example, a gas meter is installed in the residential residence, and the sampling period of the gas meter is 10 minutes. The corresponding original gas time series data can be obtained through the gas meter, for example, {11:00, 11:10, 11:20, 11:30, 11:50, 12:00, 12:10, 12:20, 12:30, 12:40, 12:50, 13:00}. Based on this, the computer can obtain the difference between 5 pairs of adjacent original timestamps, that is, the differences between the original timestamps 11:00, 11:10, 11:20, 11:30, 11:50, and 12:00 correspond to 10 minutes, 10 minutes, 10 minutes, 20 minutes, and 10 minutes, respectively. Based on this, the mode 10 minutes can be determined as the data collection frequency of the original gas time series data.

[0098] It is understandable that during the process of data collection by the energy collection device, there may be abnormal situations such as data loss or deviation in the reporting time of the sampling data. In this regard, the present application can obtain the difference between multiple pairs of adjacent original timestamps, and determine the mode of the difference between multiple pairs of adjacent original timestamps as the data sampling frequency of the original energy consumption time series data, thereby avoiding the problem of large deviation in the data sampling frequency due to abnormal situations such as data loss or deviation in the reporting time of the sampling data, which is conducive to further improving the accuracy of carbon emission monitoring.

[0099] In another exemplary embodiment, the difference between multiple pairs of adjacent original time stamps in the original energy consumption time series data is determined, and the average of the difference between multiple pairs of adjacent original time stamps is determined as the data sampling frequency of the original energy consumption time series data.

[0100] It is understandable that the energy collection device can also collect energy consumption data of the monitored place non-periodically. Based on this, there are differences in the difference between each adjacent original timestamp in the original energy time series data obtained. In this regard, the difference between multiple pairs of adjacent original timestamps in the original energy time series data can be determined, and the mode of the difference between multiple pairs of adjacent original timestamps can be determined as the data sampling frequency of the original energy time series data. For example, taking the place to be monitored as an office building, an electric meter is installed in the office building, and the electric meter is used to collect the electricity usage of the office building. If the corresponding original electricity time series data obtained through the gas meter is {00:00, 3:00, 6:00, 7:00, 8:00, 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00, 16:00, 17:00, 18:00, 19:00, 20:00, 22:00}, based on this, the computer can calculate that the average value of the difference between each pair of adjacent original timestamps is 80 minutes, and 80 minutes can be determined as the data collection frequency of the original electricity time series data.

[0101] It should be noted that the above is only an exemplary description and should not be understood as the specific limitation of the data collection frequency of the original energy consumption time series data determined by the present application based on the difference between at least two adjacent original timestamps in the original energy consumption time series data. In actual applications, other methods can also be used to determine the data collection frequency. For example, the maximum value of the difference between multiple pairs of adjacent original timestamps is determined as the data sampling frequency of the original energy consumption time series data. The specific method can be selected according to the actual situation, and no excessive limitations are made here.

[0102] S302: Determine the monitoring frequency according to the data collection frequency of each original energy consumption time series data.

[0103] In the application, the computer can collect data according to the frequency set F = {f 1 ,f 2 ,…,f i}Determine the monitoring frequency F t. For example, the monitoring frequency can be the minimum value, the least common multiple or the mode in the data collection frequency set, and the monitoring frequency can also be any other suitable value, which is not limited here. Taking the monitoring frequency as the minimum value in the data collection frequency set as an example, the minimum data collection frequency is used to align each original energy alignment data. While realizing the unification of the time series data in the time dimension, it can also improve the monitoring frequency of carbon emissions of the monitored places. Since carbon emission data is usually based on months or years as statistical dimensions, it is difficult to achieve real-time and refined management of carbon emissions. In this regard, the carbon emission monitoring method provided in the embodiment of the present application can further reduce the statistical dimension through a unified monitoring frequency. For example, minutes and seconds can be used as statistical dimensions, thereby improving the real-time and precision of carbon emission monitoring. By reasonably setting the monitoring frequency, the density of data collection and resource consumption can be balanced, the storage and computing burden caused by excessive collection can be avoided, and at the same time, the capture of key information is ensured, and the effective use of resources is achieved. It should be noted that in actual applications, other values ​​such as the least common multiple, mode, median, etc. of the data collection frequency set can also be used as the monitoring frequency, which can be set according to actual monitoring needs.

[0104] In one embodiment, Figure 4 As shown, step S302, determining the monitoring frequency according to the data collection frequency of each original energy consumption time series data, includes the following steps S401 and S402.

[0105] S401: Obtain the least common multiple corresponding to the data collection frequency of each original energy consumption time series data.

[0106] In the application, the computer can determine the data collection frequency set F={f 1 ,f 2 ,…,f i}, based on this, the lowest common multiple F of the data collection frequency set can be determined t , which can be expressed as:

[0107] F t =[f 1 ,f 2 ,…,f i ] (1)

[0108] S402: Determine a monitoring frequency according to a preset frequency coefficient and a least common multiple.

[0109] The frequency coefficient is preset, and the frequency coefficient is the ratio of the monitoring frequency to the least common multiple. Exemplarily, the frequency coefficient is a positive integer, for example, the frequency coefficient can be 1, 2, 3, or other positive integers greater than 3, which are not limited here.

[0110] It is understandable that the data collection frequency of the original energy consumption time series data may vary. Therefore, the present application adopts a unified monitoring frequency to align each piece of original energy consumption time series data. Since the monitoring frequency is a positive integer multiple of the least common multiple of the data collection frequencies of each piece of original energy consumption time series data, for each piece of original energy consumption time series data, starting from the standard time series starting point, each subsequent standard timestamp with the monitoring frequency as the alignment frequency has an original energy consumption value, and the aligned energy consumption value can be determined. That is, the original energy consumption value can be directly used to determine the aligned energy consumption value, which ensures the reliability of data processing, helps to improve the accuracy of carbon emission monitoring results, and can also improve data processing efficiency.

[0111] In one embodiment, Figure 5 As shown, step S203, according to the preset standard time series starting point and monitoring frequency, the original timestamps in multiple original energy consumption time series data are aligned, and multiple aligned energy consumption time series data are correspondingly obtained, including the following steps S501 and S502.

[0112] S501: Taking the starting point of the standard time sequence as the first standard time stamp of the standard time sequence and taking the monitoring frequency as the time interval, a standard time sequence including a plurality of standard time stamps is acquired.

[0113] The standard time series includes multiple standard timestamps, and the interval between any two adjacent standard timestamps in the standard time series is equal to the monitoring frequency. The standard time series of each aligned energy time series data is the same. The standard time series can be recorded as t standard ={t 1 ,t 2 ,…,t n}, where t n Indicates the nth standard timestamp in the standard time series. For example, if the starting point of the standard time series is set to 00:00 and the monitoring frequency is 30 minutes, the first standard timestamp is determined to be 00:00, and the second standard timestamp is determined to be 00:30, the third standard timestamp is 01:00, the fourth standard timestamp is 01:30, the fifth standard timestamp is 02:00, and so on. The interval between any two adjacent standard timestamps is 30 minutes, and these standard timestamps constitute the standard time series. The number of standard timestamps in the standard time series can be determined according to the carbon emission monitoring duration of the site to be monitored, the starting point of the standard time series and the monitoring frequency, and is not limited here.

[0114] S502: Aligning original timestamps in a plurality of pieces of original energy usage time series data according to a standard time series and a monitoring frequency, and correspondingly obtaining a plurality of pieces of aligned energy usage time series data.

[0115] The alignment energy value in the alignment energy time series data is determined based on the original energy time series data. Exemplarily, the alignment energy value is an original energy value in the original energy time series data. In another exemplary embodiment, the alignment energy value is determined based on multiple original energy values ​​in the original energy time series data.

[0116] The above carbon emission monitoring method ensures the standardization of data collection and analysis by setting a standard time series starting point and using the monitoring frequency as the time interval, so that carbon emission data from different sources or different time periods can be compared and integrated within the same time frame, improving the accuracy and comparability of the data. It not only realizes the regular collection of data, but also ensures the synchronization between multiple original energy consumption time series data. This alignment process ensures that even when facing data from different devices or locations, the time correspondence can be maintained, enhancing the consistency and reliability of the data. In addition, the aligned energy consumption time series data obtained after the alignment of the original timestamps is arranged in a unified standard time series format, which can make it easier to aggregate, analyze and count each time series data, which is conducive to providing more accurate emissions estimates, formulating more scientific and reasonable emission reduction targets and strategies, and promoting the practice of sustainable development.

[0117] In one embodiment, Figure 6 As shown, step S502, according to the standard timing and monitoring frequency, aligning the original timestamps in multiple original energy usage time series data, and correspondingly obtaining multiple aligned energy usage time series data, includes the following steps S601 to S603.

[0118] S601: Using the monitoring frequency as the length of the data alignment window, respectively determining the data alignment window of each standard timestamp.

[0119] Define the data alignment window length W = F t For example, the standard timestamp is the midpoint of the data alignment window, that is, the F before and after the standard timestamp is taken. t / 2 as the data alignment window range, the data alignment window range can be expressed as [t standard[i][n] -F t / 2,t standard[i][n] +F t / 2], where t standard[i][n] Indicates the nth standard timestamp of the i-th aligned energy usage time series data.

[0120] For example, taking the standard time sequence including 00:00, 00:30, 01:00, 01:30 and 02:00 as an example, the data alignment window range of the first standard timestamp 00:00 is [23:45, 00:15], the data alignment window range of the second standard timestamp 00:30 is [00:15, 00:45], the data alignment window range of the third standard timestamp 01:00 is [00:45, 01:15], the data alignment window range of the fourth standard timestamp 01:30 is [01:15, 01:45], and the data alignment window range of the fifth standard timestamp 02:00 is [01:45, 02:15].

[0121] S602: For any piece of original energy usage time series data, obtain the aligned energy usage value in each data alignment window in the original energy usage time series data.

[0122] For any piece of original energy consumption time series data, the original timestamps in the original energy consumption time series data that fall within each data alignment window can be determined respectively, and based on the original energy consumption values ​​corresponding to the original timestamps that fall within each data alignment window, the aligned energy consumption values ​​in the original energy consumption time series data within each data alignment window can be obtained respectively.

[0123] For example, for the first piece of original energy consumption time series data, the original timestamp that falls within the data alignment window range [23:45,00:15] of the first standard timestamp 00:00 is 00:02. Then the original energy consumption value corresponding to the original timestamp 00:02 can be determined as the aligned energy consumption value of the first piece of original energy consumption time series data at the first standard timestamp 00:00.

[0124] It should be noted that the above only takes the case where the number of original timestamps falling in the data alignment window is 1 as an example. In actual applications, there may be cases where the number is 2, 3 or other values. Specifically, the alignment energy value can be determined according to the actual situation based on the original energy value corresponding to at least one original timestamp falling in the data alignment window. For example, the average value can be taken as the alignment energy value, or the maximum value can be taken as the alignment energy value, etc., and no excessive restrictions are made here.

[0125] S603: Determine the time series data composed of the aligned energy usage values ​​in each data alignment window as the aligned energy usage time series data corresponding to the original energy usage time series data.

[0126] The above carbon emission monitoring method sets the monitoring frequency to the length of the data alignment window, ensuring that at each standard timestamp, data collection covers the complete monitoring cycle. Even if the original energy consumption time series data is discontinuous or missing, the complete time series can be filled or estimated as much as possible through the processing of the data alignment window, ensuring the continuity and integrity of the data. And. By obtaining the aligned energy consumption value in each data alignment window, the error caused by asynchronous or non-uniform sampling can be reduced, and the accuracy of the data can be improved. Especially for energy consumption data with large volatility, this method can better reflect the actual energy consumption pattern and carbon emission trend. Since the aligned data can be directly used for statistical analysis and trend prediction, no additional data preprocessing steps are required, ensuring the standardization and consistency of the data, simplifying the data analysis process, while ensuring data quality, avoiding the waste of resources caused by excessive data collection, improving efficiency, and obtaining sufficiently accurate and detailed carbon emission data to support more effective energy conservation and emission reduction strategies.

[0127] In one embodiment, Figure 7 As shown, step S602, for any piece of original energy consumption time series data, obtain the aligned energy consumption value in each data alignment window in the original energy consumption time series data, including the following steps S701 to S703.

[0128] S701: For any data alignment window, obtain the time distance between the original timestamp in the original energy usage time series data that falls within the data alignment window and the standard timestamp of the data alignment window.

[0129] For the i-th original energy consumption time series data, the time distance d between the j original timestamps and the standard timestamp that fall within the n-th data alignment window is calculated. The time distance d can be expressed as:

[0130] d [i][j] =|t standard，[i][n] -t [i][j] | (2)

[0131] Among them, d [i][j] Indicates the time distance between the j original timestamps and the standard timestamp within the nth data alignment window, t standard[i][n] Indicates the nth standard timestamp of the i-th standard sequence, t [i][j] Indicates the jth original timestamp of the i-th original energy consumption time series data.

[0132] S702: Obtain the target original timestamp corresponding to the minimum time distance.

[0133] For the standard timestamp in each data alignment window, the target original timestamp corresponding to the minimum time distance can be obtained respectively. The minimum time distance can be expressed as:

[0134] d [i][j]，min =(d [i][1] , d [i][2] ,...d [i][j] ) (3)

[0135] Among them, d [i][j]min Indicates the minimum time distance between the j original timestamps and the standard timestamp within the nth data alignment window.

[0136] S703: Determine the original energy consumption value corresponding to the target original timestamp as the aligned energy consumption value of the original energy consumption time series data within the data alignment window.

[0137] For example, for the first original energy consumption time series data, the original timestamps that fall within the data alignment window range [23:45,00:15] of the first standard timestamp 00:00 are 00:02 and 00:12. The time distance between the original timestamp 00:02 and the first standard timestamp 00:00 is 2 minutes, and the time distance between the original timestamp 00:12 and the first standard timestamp 00:00 is 12 minutes>2 minutes. The original energy consumption value corresponding to the original timestamp 00:02 is determined as the aligned energy consumption value of the first original energy consumption time series data at the first standard timestamp 00:00.

[0138] The above-mentioned carbon emission monitoring method, by calculating the time distance between the original timestamp and the standard timestamp of the data alignment window, and selecting the target original timestamp corresponding to the minimum time distance, can ensure that the selected aligned energy consumption value is closest to the time point of the data alignment window, reduce the data error introduced by time dislocation, and improve the accuracy of the monitoring data. By minimizing the time deviation, the comparison and correlation analysis between data points are more accurate, and can more truly reflect the dynamic changes of energy consumption behavior and carbon emissions. Compared with complex interpolation or data fitting methods, directly selecting the data point corresponding to the minimum time distance as the aligned energy consumption value has a smaller amount of calculation, and can complete the data alignment process faster, thereby improving the response speed and real-time performance of the entire monitoring system, simplifying the data processing process, reducing the complexity of data processing, and effectively improving the accuracy and reliability of carbon emission monitoring data, laying a solid foundation for more in-depth data analysis and decision support.

[0139] Please continue reading Figure 7 In one embodiment, the carbon emission monitoring method may further include the following steps S704 to S706.

[0140] S704: Determine a target standard timestamp, where the target standard timestamp is a standard timestamp whose number of original timestamps falling within the corresponding data alignment window is zero and / or a standard timestamp whose corresponding target original timestamp has no original energy consumption value.

[0141] In one case, if there is no original timestamp in the data alignment window in the original energy consumption time series data, the standard timestamp of the data alignment window is the target standard timestamp. For example, the data alignment window range of the first standard timestamp 00:00 is [23:45, 00:15], and the first original timestamp of the first original energy consumption time series data is 00:16. The number of original timestamps that fall within the data alignment window range [23:45, 00:15] is zero, so the first standard timestamp 00:00 is determined as the target standard timestamp.

[0142] In another case, the target original timestamp is determined based on the aforementioned step S702, and the target original timestamp does not have a corresponding original energy consumption value, and the standard timestamp corresponding to the target original timestamp is also determined as the target standard timestamp. For the first original energy consumption time series data, the original timestamps that fall within the data alignment window range [23:45,00:15] of the first standard timestamp 00:00 are 00:02 and 00:12, then the target original timestamp is 00:02, but the first original energy consumption time series data has no data at the target original timestamp 00:02, or in other words, the original energy consumption value of the target original timestamp 00:02 does not exist, then the first standard timestamp 00:00 is determined as the target standard timestamp.

[0143] When any one or more of the above situations exist, the target standard timestamp can be determined respectively. In the application, the alignment energy value corresponding to the target standard timestamp can be marked as missing data, for example, the character string "NFE" can be used to mark the missing data.

[0144] S705: Obtain two candidate original energy usage values ​​whose original timestamps are adjacent to the target standard timestamps.

[0145] In the application, the computer can first determine two candidate original timestamps adjacent to the target standard timestamp, wherein the target standard timestamp is within the interval defined by the two candidate original timestamps. Then, the computer can respectively obtain two candidate original energy consumption values ​​corresponding to the two candidate original timestamps.

[0146] For the first original energy consumption time series data, its original timestamps include 00:02, 00:12, 00:22, 00:31, and 00:42. If the target original timestamp 00:31 does not have an original energy consumption value, the second standard timestamp 00:30 is determined as the target standard timestamp, and two original timestamps 00:22 and 00:42 adjacent to the second standard timestamp 00:30 can be determined.

[0147] S706: Determine an aligned energy usage value of a target standard timestamp based on the two candidate original energy usage values.

[0148] Exemplarily, linear interpolation may be performed on two candidate original energy usage values ​​to determine an aligned energy usage value of a target standard timestamp.

[0149] Assume that the abnormal data point is (x, y), where x represents the target standard timestamp, and y represents the corrected energy consumption value corresponding to the target standard timestamp. It is known that the normal data points adjacent to the target standard timestamp are (x0, y0) and (x1, y1), where x0 and x1 represent the original timestamps, and the target standard timestamp x is in the interval [x0, x1]. y0 represents the candidate original energy consumption value corresponding to the original timestamp x0, and y1 represents the candidate original energy consumption value corresponding to the original timestamp x1. According to the linear difference, the y value corresponding to a point x in the interval [x0, x1] can be expressed by the formula:

[0150]

[0151] Since the target standard timestamp x is known, the corrected energy consumption value y corresponding to the target standard timestamp can be derived as:

[0152]

[0153] In the application, the label "NFE" can be replaced with a numerical value calculated by linear interpolation to obtain multiple aligned energy usage time series data.

[0154] The above-mentioned carbon emission monitoring method realizes effective identification of abnormal situations of missing data by identifying the target standard timestamp, and determines the aligned energy value of the target standard timestamp by finding the two candidate original energy values ​​that are closest in time to the target standard timestamp, thereby filling the data gap and ensuring the integrity, continuity, consistency and regularity of the time series data, avoiding monitoring deviations caused by incomplete data, and thus improving the accuracy and reliability of carbon emission monitoring. In addition, the embodiment of the present application reduces the need for manual intervention by automatically processing missing data and aligning timestamps, reduces the manpower cost and time cost of data processing, makes the data preparation process more efficient, effectively solves the common data missing problem in carbon emission monitoring, improves data quality and the overall effectiveness of the monitoring system, and lays the foundation for more accurate environmental management and policy formulation.

[0155] In one embodiment, Figure 8 As shown, step S204, determining the carbon emission monitoring result of the place to be monitored based on multiple aligned energy consumption time series data, may include the following steps S801 to S805.

[0156] S801: Identify abnormal data in a plurality of aligned energy usage time series data respectively, and obtain an abnormal energy usage value in each piece of aligned energy usage time series data.

[0157] Exemplarily, an abnormal data identification algorithm can be used to identify abnormal data in multiple aligned energy time series data, and obtain abnormal energy values ​​in each aligned energy time series data. Among them, the abnormal data identification algorithm can include a box plot algorithm, a KNN (K-Nearest Neighbors) algorithm, a K-Means algorithm, a neural network method, etc., which are not limited here.

[0158] S802: Determine a target standard timestamp of each abnormal energy consumption value in a standard time sequence, where the standard time sequence is determined based on a standard time sequence starting point and a monitoring frequency.

[0159] S803: For each piece of original energy usage time series data, determine two candidate original energy usage values ​​whose original timestamps are adjacent to the target standard timestamps.

[0160] The standard timing can be specifically referred to the related description of the aforementioned step S501, which will not be repeated here. Steps S802 and S803 are similar to the aforementioned step S705, and can be specifically referred to the related description, which will not be repeated here.

[0161] S804: Replace the abnormal energy consumption value according to the two candidate original energy consumption values ​​to obtain multiple standard energy consumption time series data.

[0162] In the application, the abnormal energy consumption value can be replaced according to the two candidate original energy consumption values, so as to obtain multiple standard energy consumption time series data after the replacement. For example, any one of the two candidate original energy consumption values ​​can be selected to replace the abnormal energy consumption value, or the average of the two candidate original energy consumption values ​​can be used to replace the abnormal energy consumption value, or any other suitable method can be used to replace the abnormal energy consumption value, which will not be limited too much here.

[0163] S805: Determine carbon emission monitoring results based on multiple standard energy consumption time series data.

[0164] The above-mentioned carbon emission monitoring method can identify and correct abnormal energy consumption values ​​by performing anomaly detection on aligned energy consumption time series data, thereby improving the accuracy and reliability of the overall data, and by determining two candidate original energy consumption values ​​adjacent to the original timestamp and the target standard timestamp, and replacing the abnormal energy consumption value accordingly. This refined processing method makes data repair more reasonable, reduces the risk of data distortion, and is conducive to more accurate control and optimization of energy utilization efficiency. In addition, multiple standard energy consumption time series data after the abnormal data is replaced are used as the measurement basis for subsequent carbon emission monitoring results, which reduces errors and uncertainties in the data collection process, improves the accuracy and credibility of the data, and reduces the computing resource consumption of time series data processing while ensuring real-time performance, thereby realizing accurate measurement of carbon emissions in various energy type scenarios.

[0165] In one embodiment, Fig. 9 and Fig.10 As shown, step S801, respectively identifying abnormal data in multiple aligned energy usage time series data, and obtaining the abnormal energy usage value in each aligned energy usage time series data, may include the following steps S901 to S903.

[0166] S901: Determine the first quartile, the second quartile, and the interquartile range of a plurality of aligned energy usage time series data.

[0167] For any aligned energy consumption time series data, the first quartile, the second quartile and the interquartile range can be determined respectively. Among them, the quartile can be expressed as:

[0168]

[0169] Where: i = 1, 2, 3; n represents the number of items contained in the time series data, that is, the total number of points in the aligned energy time series data. 1 represents the first quartile or the lower quartile, Q 3 represents the second quartile or the upper quartile. 2 represents the median. Then the interquartile range IQR is:

[0170] IQR=Q 3 -Q 1 (7)

[0171] S902: Determine an energy usage threshold range for each piece of aligned energy usage time series data according to the first quartile, the second quartile, and the interquartile range of each piece of aligned energy usage time series data.

[0172] In the application, the computer can determine the first energy usage threshold and the second energy usage threshold of each aligned energy usage time series data according to the first quartile, the second quartile and the interquartile range of each aligned energy usage time series data, wherein the first energy usage threshold T 上 It can be expressed as:

[0173] T 上 =Q 3 +K*IQR (8)

[0174] The second energy usage threshold T 下 It can be expressed as:

[0175] T 下 =Q 1 -K*IQR (9)

[0176] Wherein, k represents the box plot parameter, and its size is adjustable. For example, k=1.5.

[0177] Based on the above, the energy usage threshold range may be expressed as being greater than or equal to the second energy usage threshold, and less than or equal to the first energy usage threshold.

[0178] S903: In each alignment energy usage time series data, an alignment energy usage value exceeding a corresponding energy usage threshold range is determined as an abnormal energy usage value.

[0179] In each alignment energy usage time series data, an alignment energy usage value greater than the first energy usage threshold or less than the second energy usage threshold is determined as an abnormal energy usage value. In an application, the computer can mark all abnormal energy usage values, for example, the abnormal energy usage values ​​can be uniformly marked as "NFE", or marked with other symbols, which are not limited here.

[0180] The above-mentioned carbon emission monitoring method uses a box plot algorithm to filter and clean each aligned energy consumption time series data, that is, by calculating the first quartile (lower quartile, Q1), the second quartile (upper quartile, Q3) and the interquartile range (IQR) of each aligned energy consumption time series data, and determining the energy consumption threshold range based on the quartiles and the interquartile range, so that the energy consumption threshold range can be used to objectively determine whether each aligned energy consumption value in each aligned energy consumption time series data is abnormal. This method does not require prior knowledge of the distribution type of the data and is applicable to various types of energy consumption data. It improves the versatility and adaptability of anomaly detection, and can accurately identify abnormal energy consumption values, rather than mistakenly marking them as abnormal simply because the data fluctuates greatly, which helps to reduce the false positive rate and avoid misjudging normal high or low values ​​as abnormalities, thereby improving the accuracy of the monitoring results. In addition, the energy consumption threshold range can be flexibly adjusted according to actual conditions. For example, by setting different IQR multiples, namely the box plot parameter k, to expand or narrow the threshold interval. This configurability enables the monitoring system to adapt to energy consumption patterns in different scenarios, improve the pertinence and effectiveness of anomaly detection, and help to further improve the accuracy of carbon emission monitoring.

[0181] In one embodiment, Fig.11 As shown, step S804, according to two candidate original energy consumption values, replaces the abnormal energy consumption value to obtain multiple standard energy consumption time series data, which may include the following steps S1101 and S1102.

[0182] S1101: Linearly interpolate two candidate original energy usage values ​​to obtain a corrected energy usage value for each abnormal energy usage value.

[0183] S1102: Replace the abnormal energy consumption value according to the corrected energy consumption value to obtain multiple standard energy consumption time series data.

[0184] Step S1101 and step S1102 are similar to the aforementioned step S706. For details, please refer to the aforementioned related description, which will not be repeated here.

[0185] The above-mentioned carbon emission monitoring method calculates the corrected energy consumption value of the abnormal energy consumption value through linear interpolation and performs replacement processing, which can effectively fill the abnormal or missing values ​​in the data set and restore the integrity and continuity of the data. By correcting the abnormal values ​​through linear interpolation, the impact of these abnormal points on the overall data analysis can be reduced, making the data closer to the actual situation, improving the accuracy of the decision-making basis, and providing solid data support for more in-depth environmental monitoring and management decisions.

[0186] In one embodiment, step S204, determining the carbon emission monitoring results of the place to be monitored based on multiple aligned energy usage time series data, may include: determining the carbon emission monitoring results of the place to be monitored based on multiple aligned energy usage time series data and the carbon emission factors of multiple aligned energy usage time series data.

[0187] In the application, the computer can calculate the total carbon emissions of the monitored site using the carbon emission factor method according to a pre-set standard such as the GHG PROTOCOL standard. Specifically, the computer can search for the carbon emission factors corresponding to various types of energy consumption in the carbon emission factor library, and bring them into the formula calculation together with the aligned energy consumption time series data of the energy consumption, and then aggregate the carbon emission data corresponding to various types of energy consumption according to the monitoring frequency to obtain the total carbon emissions. Among them, the carbon emission factor library can be stored in Figure 1 In the data storage system shown, the corresponding carbon emission factor can be called from the data storage system to calculate the total carbon emission. The total carbon emission can be expressed as:

[0188] E=∑(Q i *C i ) (10)

[0189] Among them, E is the total carbon emission, Q i is the activity data of the ith substance, i.e., the emission intensity data or the aligned energy value; C i is the carbon emission factor corresponding to the substance. For example, for emission sources in scope 2 or 3 of the GHG PROTOCOL standard, Q i The data of the collector can be directly obtained, such as electricity consumption data; for emission sources in scope 1, Q i for:

[0190] Q i =Q L，i *N i (11)

[0191] Among them, Q L,i is the lower heating value of the ith substance, N i is the consumption of the ith substance (i.e., collector data). The final total carbon emission time series format is shown in Table 1 below.

[0192] Table 1

[0193] Sequence number 1 2 3 4 5 … Standard timing timestamp T1 T2 T3 T4 T5 … Standard timing corresponding value E1 E2 E3 E4 E5 …

[0194] The above-mentioned carbon emission monitoring method, by combining multiple aligned energy consumption time series data with their respective carbon emission factors, can accurately calculate the carbon emissions at each time point or time period, overcomes the shortcomings of traditional extensive carbon emission estimation, and provides more detailed and accurate carbon emission data. In addition, the use of aligned energy consumption time series data allows real-time or near real-time monitoring of carbon emissions, so abnormal emissions or high emission periods can be discovered in time, providing a basis for rapid emission reduction measures, achieving refined management of carbon emission sources, and optimizing energy utilization efficiency and reducing carbon footprint in a targeted manner. This method not only improves the accuracy and real-time nature of carbon emission monitoring results, but also promotes the realization of energy conservation and emission reduction goals, strengthens compliance and reporting capabilities, and helps promote the realization of environmental protection and sustainable development goals.

[0195] In one embodiment, Fig.12 As shown, the multiple aligned energy usage time series data include first aligned energy usage time series data of a first energy type and second aligned energy usage time series data of a second energy type. The first energy type is different from the second energy type. For example, the first energy type is electric energy, and the second energy type is gas. Based on this, the aforementioned step: determining the carbon emission monitoring result of the place to be monitored based on the multiple aligned energy usage time series data and the carbon emission factor of the multiple aligned energy usage time series data may include the following steps S1201 to S1203.

[0196] S1201: Determine a first carbon emission monitoring result of the place to be monitored according to the first aligned energy consumption time series data and the first carbon emission factor.

[0197] S1202: Determine a second carbon emission monitoring result of the place to be monitored based on the second aligned energy consumption time series data and the second carbon emission factor.

[0198] S1203: Determine the total carbon emission monitoring result of the place to be monitored according to the first carbon emission monitoring result and the second carbon emission monitoring result.

[0199] For example, according to the GHG PROTOCOL standard, the carbon emission factor method is used for calculation. Among them, the carbon emission of electricity consumption belongs to Scope 2, and the emission amount is the multiplication of the standard time series value of electricity consumption and the carbon emission factor of purchased electricity. The formula can be expressed as follows:

[0200] E elec =C elec *∑Q elec (12)

[0201] Among them, E elec Expressed as carbon emissions from electricity consumption, Q elec is the activity data of the ith substance, i.e., the energy value of the electricity consumption; C i is the carbon emission factor corresponding to electricity consumption.

[0202] Natural gas carbon emissions belong to Scope 1. The emissions are the product of the standard time series value of gas consumption, the lower calorific value of natural gas and the natural gas carbon emission factor. The formula can be expressed as follows:

[0203] E natural =C natureal *Q L，natural *∑N natural (13)

[0204] Among them, E natural Expressed as carbon emissions from gas consumption, C natural is the carbon emission factor corresponding to the gas consumption, Q L,natural is the lower heating value of the ith substance, N natural is the consumption of the ith substance, that is, the aligned energy value of the gas consumption.

[0205] The total carbon emission E can be expressed as:

[0206] E=E elec +E natural (14)

[0207] The above-mentioned carbon emission monitoring method ensures that the monitoring covers all major energy consumption by separately processing the aligned energy consumption time series data of the first energy type (such as electricity) and the second energy type (such as gas), avoiding the underestimation of overall carbon emissions due to ignoring a certain type of energy. It can not only clearly show the contribution ratio of different energy types to carbon emissions, but also aggregate carbon emissions of multiple energy types to achieve statistics on total carbon emissions of the monitored sites, increase data transparency, provide more detailed and comprehensive carbon emission data, support multi-dimensional analysis, help identify major emission sources and optimize emission reduction strategies, and help enterprises optimize their energy mix, such as increasing the proportion of clean energy and reducing the use of high-carbon emission energy, thereby reducing overall carbon emissions while meeting production needs.

[0208] In one embodiment, Fig.13 As shown, a carbon emission monitoring method is provided for application in Figure 1 Taking the computer shown as an example, monitoring the total carbon emissions of an enterprise in a certain month, the method includes the following steps S1301 to S1306.

[0209] S1301: Input multiple pieces of original energy consumption time series data.

[0210] Obtain the raw energy consumption time series data of 3 electricity meters and 2 gas meters for a whole month.

[0211] S1302: Determine the monitoring frequency according to the data collection frequency of the plurality of original energy consumption time series data.

[0212] Determine the data collection frequency set for 3 electricity meters and 2 gas meters. The data collection frequency set F is shown in Table 2. The lowest common multiple of each data collection frequency in the data collection frequency set is determined as the monitoring frequency.

[0213] Table 2

[0214] Equipment No. Electricity Meter 01 Electricity Meter 02 Electricity Meter 03 Gas Meter 01 Gas Meter 02 Collection frequency 5min 15min 5min 10min 10min

[0215] Combination Fig.14 It can be found that if the monitoring frequency is 5 minutes, there will be two null values ​​for every three monitoring points of the electricity meter 02, and one null value for every two monitoring points of the gas meter 01; if the monitoring frequency is 10 minutes, there will still be two null values ​​for every three monitoring points of the electricity meter 02, and other devices can meet the requirements. Therefore, the lowest common multiple of the data collection frequency of 30 minutes is taken as the monitoring frequency Ft.

[0216] S1303: Align multiple pieces of original energy usage time series data according to a preset standard time series starting point and a monitoring frequency, and obtain multiple pieces of aligned energy usage time series data.

[0217] Considering that there may be differences in the reporting time of data transmission, after determining the monitoring frequency, it is necessary to align the timestamps of multiple time series data. We set the hour as the starting point of the standard time series, and use the monitoring frequency as the time interval to align the timestamps of each time series data with reference to the standard time series. Taking a time series i as an example, the collection frequency of the series is 10 minutes, and the timestamps and corresponding values ​​of a certain continuous data segment are shown in Table 3 below.

[0218] Table 3

[0219] Sequence number <![CDATA[t i1 ]]> <![CDATA[t i2 ]]> <![CDATA[t i3 ]]> <![CDATA[t i4 ]]> <![CDATA[t i5 ]]> … Original timestamp (hh:mm) 00:02 00:12 00:22 00:31 00:42 … Original energy value 259.4 261.2 261.9 263.1 264.7 …

[0220] Combination Fig.15 As shown, the data alignment window length W = Ft = 30min, and the data alignment window range is [t standard[i][n] -15,t standard[i][n] +15], taking the standard timestamp 00:30 as an example, the window range is [00:15,00:45], and the timestamps falling within this range include t i3 , t i4 and t i5 , where the closest point is t i4 , so take t i4 The original energy consumption value is used as the alignment energy consumption value of the i-th alignment energy consumption time series data at the standard timestamp 00:30, which is 263.1.

[0221] If there is no data point within the window range, the string "NFE" is used to fill in the corresponding value of the standard time series timestamp. By using this method to shift the data alignment window backward, the original energy time series data i can be obtained, and the i-aligned energy time series data i after alignment processing can be obtained. standard As shown in Table 4 below.

[0222] Table 4

[0223]

[0224] S1304: Using a box plot algorithm to identify abnormal data on multiple aligned energy consumption time series data, and replace abnormal data and missing data to obtain multiple standard energy consumption time series data.

[0225] After obtaining the aligned energy time series data based on the above step S1304, the outliers of these series are processed using a box plot. Fig.16 The following are the results of the box plot analysis of 5 aligned energy time series data. When the aligned energy value is greater than the upper threshold of the box plot or less than the lower threshold of the box plot, it is determined to be an abnormal energy value. All abnormal energy values ​​are uniformly marked with "NFE". All missing data points and abnormal points marked with "NFE" are processed one by one and replaced with linear interpolation. The data before and after processing are compared. Fig.17 and Fig.18 shown.

[0226] S1305: Aggregate multiple standard energy consumption time series data according to the monitoring frequency to obtain total carbon emissions.

[0227] According to the GHG PROTOCOL standard, the carbon emission factor method is used, that is, the carbon emissions from electricity are calculated using the above formula (12), and the carbon emissions from natural gas are calculated using the above formula (13), and then the total carbon emissions E are calculated using the above formula (14).

[0228] This application uses data processing technology to filter and clean the collected energy consumption and carbon emission data, reduce errors and uncertainties in the data collection process, and improve the accuracy and credibility of the data; and introduces data from more monitoring points and monitoring time periods to improve the real-time monitoring, help companies discover problems in a timely manner and take countermeasures, and effectively improve carbon emission management efficiency; in addition, based on the collection frequency and time series data characteristics of various energy consumption collectors, aggregation is performed to reduce the computing resource consumption of time series data processing while ensuring real-time performance, thereby achieving accurate carbon emission measurement in scenarios where companies use multiple energy types.

[0229] It should be understood that, although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0230] Based on the same inventive concept, the embodiment of the present application also provides a carbon emission monitoring device for implementing the carbon emission monitoring method involved above. The implementation scheme for solving the problem provided by the carbon emission monitoring device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more carbon emission monitoring device embodiments provided below can refer to the limitations of the carbon emission monitoring method above, and will not be repeated here.

[0231] In one embodiment, Fig.19 As shown, a carbon emission monitoring device 1900 is provided, including: an acquisition module 1901, an alignment module 1902 and a determination module 1903. Among them, the acquisition module 1901 is used to obtain multiple original energy consumption time series data in the place to be monitored. The alignment module 1902 is used to obtain the monitoring frequency of the place to be monitored based on the multiple original energy consumption time series data, and align the original timestamps in the multiple original energy consumption time series data according to the preset standard time series starting point and monitoring frequency, and correspondingly obtain multiple aligned energy consumption time series data. The determination module 1903 is used to determine the carbon emission monitoring results of the place to be monitored based on the multiple aligned energy consumption time series data.

[0232] In one embodiment, the alignment module 1902 is also used to determine the difference between any two adjacent original timestamps in any original energy consumption time series data as the data collection frequency of the original energy consumption time series data; and determine the monitoring frequency according to the data collection frequency of each original energy consumption time series data.

[0233] In one embodiment, the alignment module 1902 is further used to obtain the least common multiple corresponding to the data collection frequency of each original energy consumption time series data; and determine the monitoring frequency according to the preset frequency coefficient and the least common multiple.

[0234] In one embodiment, the alignment module 1902 is also used to use the standard timing starting point as the first standard timestamp of the standard timing, and the monitoring frequency as the time interval to obtain a standard timing including multiple standard timestamps; according to the standard timing and the monitoring frequency, the original timestamps in multiple original energy consumption timing data are aligned, and multiple aligned energy consumption timing data are obtained accordingly.

[0235] In one embodiment, the alignment module 1902 is also used to use the monitoring frequency as the length of the data alignment window to determine the data alignment window of each standard timestamp respectively; for any original energy consumption time series data, obtain the alignment energy consumption value in each data alignment window in the original energy consumption time series data; and determine the time series data composed of the alignment energy consumption values ​​in each data alignment window as the alignment energy consumption time series data corresponding to the original energy consumption time series data.

[0236] In one embodiment, the alignment module 1902 is also used to obtain, for any data alignment window, the time distance between the original timestamp in the original energy consumption time series data that falls within the data alignment window and the standard timestamp of the data alignment window; obtain the target original timestamp corresponding to the minimum time distance; and determine the original energy consumption value corresponding to the target original timestamp as the aligned energy consumption value of the original energy consumption time series data within the data alignment window.

[0237] In one embodiment, the alignment module 1902 is also used to determine a target standard timestamp, which is a standard timestamp whose number of original timestamps falling within the corresponding data alignment window is zero, and / or a standard timestamp whose corresponding target original timestamp does not have an original energy consumption value; determine two candidate original energy consumption values ​​adjacent to the original timestamp and the target standard timestamp; and determine the alignment energy consumption value of the target standard timestamp based on the two candidate original energy consumption values.

[0238] In one embodiment, the determination module 1903 is also used to identify the abnormal data in multiple aligned energy consumption time series data respectively, and obtain the abnormal energy consumption value in each aligned energy consumption time series data; obtain the target standard timestamp of each abnormal energy consumption value in the standard time series, and the standard time series is determined according to the starting point of the standard time series and the monitoring frequency; for each original energy consumption time series data, determine two candidate original energy consumption values ​​adjacent to the original timestamp and the target standard timestamp; replace the abnormal energy consumption value according to the two candidate original energy consumption values ​​to obtain multiple standard energy consumption time series data; determine the carbon emission monitoring results based on the multiple standard energy consumption time series data.

[0239] In one embodiment, the determination module 1903 is also used to determine the first quartile, the second quartile and the interquartile range of multiple aligned energy usage time series data; determine the energy usage threshold range of each aligned energy usage time series data based on the first quartile, the second quartile and the interquartile range of each aligned energy usage time series data; in each aligned energy usage time series data, the aligned energy usage value that exceeds the corresponding energy usage threshold range is determined as an abnormal energy usage value.

[0240] In one embodiment, the determination module 1903 is further used to perform linear interpolation on two candidate original energy consumption values ​​to obtain a corrected energy consumption value for each abnormal energy consumption value; replace the abnormal energy consumption value according to the corrected energy consumption value to obtain multiple standard energy consumption time series data.

[0241] In one embodiment, the determination module 1903 is further used to determine the carbon emission monitoring result of the site to be monitored based on the multiple aligned energy usage time series data and the carbon emission factors of the multiple aligned energy usage time series data.

[0242] In one embodiment, the plurality of aligned energy usage time series data includes first aligned energy usage time series data of a first energy type and second aligned energy usage time series data of a second energy type. The determination module 1903 is further used to determine a first carbon emission monitoring result of the place to be monitored based on the first aligned energy usage time series data and the first carbon emission factor; determine a second carbon emission monitoring result of the place to be monitored based on the second aligned energy usage time series data and the second carbon emission factor; and determine a total carbon emission monitoring result of the place to be monitored based on the first carbon emission monitoring result and the second carbon emission monitoring result.

[0243] Based on the same inventive concept, the embodiment of the present application also provides a carbon emission monitoring system for implementing the carbon emission monitoring device involved above. The implementation scheme for solving the problem provided by the carbon emission monitoring system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more carbon emission monitoring system embodiments provided below can refer to the limitations on the carbon emission monitoring device above, and will not be repeated here.

[0244] Each module in the carbon emission monitoring device 1900 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0245] In one embodiment, Fig. 20As shown, the carbon emission monitoring system includes the carbon emission monitoring device 1900 and multiple energy collection devices 2000 provided in the above-mentioned embodiment. Among them, the energy collection device 2000 is arranged at the place to be monitored, and the energy collection device 2000 is used to collect the original energy consumption time series data. Exemplarily, the energy collection device includes at least one of the power data collection device 2001, the gas data collection device 2002, and the steam data collection device 2003. The number and type of energy collection devices 2000 can be determined according to the place to be monitored, and are not limited here. Exemplarily, the carbon emission monitoring system may also include a communication device 3000, and the communication device 3000 is respectively connected to the carbon emission monitoring device 1900 and the energy collection device 2000. The carbon emission monitoring device 1900 can establish a communication connection with the energy collection device 2000 through the communication module 3000, so that the carbon emission monitoring device 1900 can obtain multiple pieces of original energy consumption time series data from multiple energy collection devices 2000. Based on this, multiple energy collection devices 2000 can be arranged in different local areas of the monitored place, so that real-time monitoring of a certain energy consumption in the local areas of the monitored place can be carried out through multiple energy collection devices 2000, thereby realizing the monitoring of carbon emissions of the monitored place based on multiple original energy consumption time series data generated by the energy collection device. It is not limited by the monitored place, has a wide range of applicability, and improves the real-time nature of carbon emission monitoring.

[0246] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.21 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a carbon emission monitoring method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0247] Those skilled in the art will understand that Fig.21The 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0248] In one 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 steps of the aforementioned carbon emission monitoring method when executing the computer program.

[0249] 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 steps of the aforementioned carbon emission monitoring method are implemented.

[0250] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the aforementioned carbon emission monitoring method when executed by a processor.

[0251] It should be noted that the user information (including but not limited to user device information, user profile 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.

[0252] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0253] The technical features of the above embodiments may be combined arbitrarily. 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 specification.

[0254] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A carbon emission monitoring method, characterized in that: The method comprises: Obtain multiple pieces of original energy consumption time series data of the place to be monitored; Acquire the monitoring frequency of the place to be monitored according to the plurality of original energy consumption time series data; According to a preset standard time series starting point and the monitoring frequency, the original timestamps in the multiple pieces of original energy consumption time series data are aligned, and a plurality of aligned energy consumption time series data are correspondingly obtained; The carbon emission monitoring result of the place to be monitored is determined based on the multiple aligned energy consumption time series data.

2. The method according to claim 1, characterized in that The acquiring the monitoring frequency of the place to be monitored according to the plurality of original energy consumption time series data includes: For any piece of original energy consumption time series data, determine the data collection frequency of the original energy consumption time series data according to the difference between at least two adjacent original timestamps in the original energy consumption time series data; The monitoring frequency is determined according to the data collection frequency of each of the original energy consumption time series data.

3. The method according to claim 2, characterized in that The step of determining the monitoring frequency according to the data collection frequency of each of the original energy consumption time series data includes: Obtaining the least common multiple of the data collection frequencies of the original energy consumption time series data; The monitoring frequency is determined according to a preset frequency coefficient and the least common multiple.

4. The method according to any one of claims 1 to 3, characterized in that: The aligning of the original timestamps in the plurality of original energy usage time series data according to the preset standard time series starting point and the monitoring frequency, and correspondingly obtaining a plurality of aligned energy usage time series data, includes: Taking the starting point of the standard time sequence as the first standard timestamp of the standard time sequence and taking the monitoring frequency as the time interval, obtaining a standard time sequence including a plurality of standard timestamps; According to the standard time series and the monitoring frequency, the original timestamps in the multiple pieces of original energy usage time series data are aligned, and the multiple pieces of aligned energy usage time series data are correspondingly obtained.

5. The method according to claim 4, characterized in that The aligning of original timestamps in the plurality of pieces of original energy usage time series data according to the standard time series and the monitoring frequency, and correspondingly acquiring the plurality of pieces of aligned energy usage time series data, includes: Taking the monitoring frequency as the length of the data alignment window, respectively determining the data alignment window of each of the standard timestamps; For any piece of original energy usage time series data, obtaining an aligned energy usage value in each of the data alignment windows in the original energy usage time series data; The time series data composed of the alignment energy values ​​in each of the data alignment windows is determined as the alignment energy time series data corresponding to the original energy time series data.

6. The method according to claim 5, characterized in that The step of obtaining, for any piece of original energy usage time series data, an aligned energy usage value in each of the data alignment windows in the original energy usage time series data includes: For any data alignment window, obtaining the time distance between the original timestamp in the original energy usage time series data that falls within the data alignment window and the standard timestamp of the data alignment window; Get the target original timestamp corresponding to the minimum time distance; The original energy consumption value corresponding to the target original timestamp is determined as the aligned energy consumption value of the original energy consumption time series data within the data alignment window.

7. The method according to claim 6, characterized in that The method further comprises: Determine a target standard timestamp, where the target standard timestamp is a standard timestamp whose number of original timestamps falling within the corresponding data alignment window is zero and / or a standard timestamp whose corresponding target original timestamp does not have an original energy consumption value; Determine two candidate original energy usage values ​​whose original timestamps are adjacent to the target standard timestamp; An aligned energy usage value of the target standard timestamp is determined based on the two candidate original energy usage values.

8. The method according to any one of claims 1 to 3, characterized in that: The determining the carbon emission monitoring result of the place to be monitored based on the plurality of aligned energy usage time series data includes: Identify the abnormal data in the plurality of alignment energy usage time series data respectively, and obtain the abnormal energy usage value in each alignment energy usage time series data; Obtaining a target standard timestamp of each of the abnormal energy consumption values ​​in a standard time sequence, wherein the standard time sequence is determined according to the standard time sequence starting point and the monitoring frequency; For each piece of original energy consumption time series data, determine two candidate original energy consumption values ​​whose original timestamps are adjacent to the target standard timestamp; The abnormal energy consumption value is replaced according to the two candidate original energy consumption values ​​to obtain a plurality of standard energy consumption time series data; The carbon emission monitoring result is determined based on the multiple standard energy consumption time series data.

9. The method according to claim 8, characterized in that The step of respectively identifying the abnormal data in the plurality of alignment energy usage time series data and obtaining the abnormal energy usage value in each alignment energy usage time series data includes: Determine a first quartile, a second quartile, and an interquartile range of the plurality of aligned energy usage time series data; Determine an energy usage threshold range for each piece of the aligned energy usage time series data according to the first quartile, the second quartile and the interquartile range of each piece of the aligned energy usage time series data; In each piece of the alignment energy usage time series data, an alignment energy usage value exceeding a corresponding energy usage threshold range is determined as the abnormal energy usage value.

10. The method according to claim 8, characterized in that The replacing process of the abnormal energy consumption value according to the two candidate original energy consumption values ​​to obtain a plurality of standard energy consumption time series data includes: Performing linear interpolation on the two candidate original energy consumption values ​​to obtain a corrected energy consumption value of each abnormal energy consumption value; The abnormal energy consumption value is replaced according to the corrected energy consumption value to obtain the plurality of standard energy consumption time series data.

11. The method according to claim 1, characterized in that: The determining the carbon emission monitoring result of the place to be monitored based on the plurality of aligned energy usage time series data includes: Based on the multiple aligned energy usage time series data and the carbon emission factors of the multiple aligned energy usage time series data, the carbon emission monitoring result of the place to be monitored is determined.

12. The method according to claim 11, characterized in that The plurality of aligned energy usage time series data include first aligned energy usage time series data of a first energy type and second aligned energy usage time series data of a second energy type, and determining the carbon emission monitoring result of the to-be-monitored place based on the plurality of aligned energy usage time series data and the carbon emission factor of the plurality of aligned energy usage time series data includes: Determining a first carbon emission monitoring result of the place to be monitored according to the first aligned energy consumption time series data and the first carbon emission factor; Determine a second carbon emission monitoring result of the place to be monitored according to the second aligned energy consumption time series data and the second carbon emission factor; The total carbon emission monitoring result of the place to be monitored is determined according to the first carbon emission monitoring result and the second carbon emission monitoring result.

13. A carbon emission monitoring device, characterized in that: include: An acquisition module is used to acquire multiple pieces of original energy consumption time series data in the place to be monitored; An alignment module, used to obtain the monitoring frequency of the place to be monitored according to the multiple original energy consumption time series data, and align the original timestamps in the multiple original energy consumption time series data according to a preset standard time series starting point and the monitoring frequency, and correspondingly obtain multiple aligned energy consumption time series data; A determination module is used to determine the carbon emission monitoring result of the place to be monitored based on the multiple aligned energy consumption time series data.

14. A carbon emission monitoring system, characterized in that: include: A plurality of energy collection devices, each of which is arranged at a location to be monitored and is used to collect raw energy usage time series data; The carbon emission monitoring device as described in claim 13, wherein the carbon emission monitoring device is connected to the multiple energy collection devices respectively.

15. 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 12 are implemented.

16. 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 12 are implemented.

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