Enterprise carbon emission data management method, system, equipment and medium

Abstract

The invention provides an enterprise carbon emission data management method, system and device and a medium, and the method comprises the steps: collecting original data related to carbon emission, and carrying out the screening to obtain to-be-processed data; performing validity judgment on the to-be-processed data to obtain first intermediate data and first abnormal data; after the first abnormal data are classified, different measures are taken for data processing, and second intermediate data are obtained; the first intermediate data and the second intermediate data are merged to obtain first data; performing rationality judgment on the first data to obtain third intermediate data and second abnormal data; classifying the second abnormal data, and performing data processing by adopting different measures to obtain fourth intermediate data; merging the third intermediate data and the fourth intermediate data to obtain second data; performing validity judgment and rationality judgment on the second data in sequence; under the condition that the second data meet the condition, the second data serve as cleaned data to be subjected to carbon emission calculation. According to the scheme, the reliability of the cleaned data can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of carbon emission data management, and in particular to a method, system, device and medium for enterprise carbon emission data management. Background Art

[0002] Nowadays, it is a general trend to accelerate the promotion of green and low-carbon development and achieve carbon peak and carbon neutrality. How various organizations can formulate carbon peak and carbon neutrality routes and development plans that are in line with their own characteristics and meet the needs of social development, and achieve carbon peak and carbon neutrality reasonably and orderly, is a very realistic and urgent issue at present.

[0003] In the current corporate carbon emission data management process, for the data cleaning part, direct deletion is usually used to deal with abnormal data. The single processing method is likely to cause the loss of important data or the cleaned data not meeting the requirements, which in turn affects the reliability of the cleaned data and the integrity of the valid data involved in the carbon emission calculation, thereby affecting the accuracy of the carbon emission data processing results. Summary of the invention

[0004] In order to overcome the defects of the above-mentioned prior art, the present application provides an enterprise carbon emission data management method, system, equipment and medium, adopts different processing methods for different abnormal data, and further judges the cleaned data, which can improve the reliability of the cleaned data and achieve the effect of improving the integrity of effective data and the accuracy of carbon emission calculations.

[0005] In order to solve the above technical problems, this application provides the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a method for managing enterprise carbon emission data is provided, comprising:

[0007] Collecting raw data related to carbon emissions; the raw data carries raw attribute information;

[0008] Acquire a preset data range; the preset data range carries preset attribute information;

[0009] Matching the original attribute information with the preset attribute information to determine a data range corresponding to the original data;

[0010] Screening the original data based on the data range to obtain data to be processed that meets the data range;

[0011] Performing validity judgment on the data to be processed to obtain first intermediate data that meets a preset validity condition and first abnormal data that does not meet the preset validity condition, and marking the first abnormal data so that the first abnormal data carries a first abnormal mark;

[0012] classifying the first abnormal data according to the abnormal type of the first abnormal data to obtain a first classification result;

[0013] Taking different measures to process the first classification result to obtain second intermediate data;

[0014] Merging the first intermediate data and the second intermediate data to obtain first data;

[0015] Performing a rationality judgment on the first data to obtain third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and marking the second abnormal data so that the second abnormal data carries a second abnormal mark;

[0016] classifying the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result;

[0017] Taking different measures to process the second classification result to obtain fourth intermediate data;

[0018] Combining the third intermediate data with the fourth intermediate data to obtain second data;

[0019] Performing the validity judgment and the rationality judgment on the second data in sequence;

[0020] In the case where the second data meets the preset validity condition and the preset rationality condition, the second data is used as cleaned data to perform carbon emission calculation based on the cleaned data.

[0021] In an exemplary embodiment, the validity judgment includes a data type check, a data format check, a data valid range check, a data uniqueness check, a data consistency check, and a data integrity check.

[0022] In an exemplary embodiment, the first classification result includes type abnormal data, format abnormal data, effective range abnormal data, uniqueness abnormal data, consistency abnormal data and integrity abnormal data.

[0023] In an exemplary embodiment, taking different measures to process the data of the first classification result includes:

[0024] The type abnormal data and the format abnormal data are standardized based on preset standards; the effective range abnormal data are deleted or manually processed; the uniqueness abnormal data are deleted; the consistency abnormal data are checked; the integrity abnormal data are deduced or manually processed.

[0025] In an exemplary embodiment, the rationality judgment includes data comprehensiveness check, data reasonable range check, data logic check and data real-time check.

[0026] In an exemplary embodiment, the second classification result includes comprehensive abnormal data, reasonable range abnormal data, logical abnormal data and real-time abnormal data.

[0027] In an exemplary embodiment, taking different measures to process the data of the second classification result includes:

[0028] The comprehensive abnormal data and the real-time abnormal data are checked and processed; the abnormal data within the reasonable range is deleted or manually processed; and the logical abnormal data is manually processed.

[0029] According to a second aspect of an embodiment of the present application, there is provided an enterprise carbon emission data management system, the system comprising:

[0030] A raw data collection module, used to collect raw data related to carbon emissions; the raw data carries raw attribute information;

[0031] A preset range acquisition module, used to acquire a preset data range; the preset data range carries preset attribute information;

[0032] An attribute matching module, used to match the original attribute information with the preset attribute information to determine a data range corresponding to the original data;

[0033] A data screening module, used for screening the original data based on the data range to obtain the data to be processed that meets the data range;

[0034] A validity judgment module, used to judge the validity of the data to be processed, obtain first intermediate data that meets the preset validity condition and first abnormal data that does not meet the preset validity condition, and mark the first abnormal data so that the first abnormal data carries a first abnormal mark;

[0035] A first classification module, configured to classify the first abnormal data according to an abnormal type of the first abnormal data to obtain a first classification result;

[0036] A first data processing module, used for taking different measures to process the first classification result to obtain second intermediate data;

[0037] A first data merging module, used for merging the first intermediate data and the second intermediate data to obtain first data;

[0038] a rationality judgment module, configured to perform a rationality judgment on the first data, obtain third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and perform a marking process on the second abnormal data so that the second abnormal data carries a second abnormal mark;

[0039] A second classification module, used for classifying the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result;

[0040] A second data processing module, used for taking different measures to process the second classification result to obtain fourth intermediate data;

[0041] A second data merging module, used for merging the third intermediate data and the fourth intermediate data to obtain second data;

[0042] a second judgment module, used for sequentially performing the validity judgment and the rationality judgment on the second data;

[0043] The cleaned data determination module is used to use the second data as cleaned data when the second data meets the preset validity condition and the preset rationality condition, so as to calculate carbon emissions through the cleaned data.

[0044] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement any of the above-mentioned enterprise carbon emission data management methods.

[0045] According to the fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored. The at least one instruction or the at least one program is loaded and executed by a processor to implement any of the above-mentioned enterprise carbon emission data management methods.

[0046] By adopting the above technical solution, the present application has the following beneficial effects:

[0047] The present application provides a method, system, device and medium for managing enterprise carbon emissions data. Different processing methods are adopted for different abnormal data according to the characteristics of various abnormal data, so as to avoid the loss of important data due to a single processing method, thereby affecting the reliability of the cleaned data. In addition, the cleaned data is further judged for validity and rationality to ensure that the cleaned data meets the requirements and can improve the reliability of the cleaned data, thereby achieving the effect of improving the integrity of effective data and the accuracy of carbon emissions calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flowchart of a method for managing enterprise carbon emissions data provided in an embodiment of the present application;

[0050] Figure 2 A structural block diagram of an enterprise carbon emission data management system provided in an embodiment of the present application;

[0051] Figure 3 A hardware structure block diagram of an electronic device that runs a method for managing enterprise carbon emissions data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "top", "bottom", etc. is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may include one or more of the features explicitly or implicitly. Moreover, the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here.

[0054] See also Figure 1 , which is a flow chart of a method for managing enterprise carbon emission data provided by an embodiment of the present application, the method for managing enterprise carbon emission data includes:

[0055] Step 101: Collecting raw data related to carbon emissions; the raw data carries raw attribute information;

[0056] Step 102: Acquire a preset data range; the preset data range carries preset attribute information;

[0057] Step 103: Match the original attribute information with the preset attribute information to determine the data range corresponding to the original data;

[0058] Step 104: Filter the original data based on the data range to obtain the data to be processed that meets the data range;

[0059] Step 105: performing validity judgment on the data to be processed, obtaining first intermediate data that meets the preset validity condition and first abnormal data that does not meet the preset validity condition, and marking the first abnormal data so that the first abnormal data carries a first abnormal mark;

[0060] Step 106: classify the first abnormal data according to the abnormal type of the first abnormal data to obtain a first classification result;

[0061] Step 107: taking different measures to process the first classification result to obtain second intermediate data;

[0062] Step 108: merging the first intermediate data and the second intermediate data to obtain first data;

[0063] Step 109: performing a rationality judgment on the first data, obtaining third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and marking the second abnormal data so that the second abnormal data carries a second abnormal mark;

[0064] Step 110: classifying the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result;

[0065] Step 111: taking different measures to process the second classification result to obtain fourth intermediate data;

[0066] Step 112: merging the third intermediate data and the fourth intermediate data to obtain second data;

[0067] Step 113: performing validity judgment and rationality judgment on the second data in sequence;

[0068] Step 114: When the second data meets the preset validity condition and the preset rationality condition, the second data is used as the cleaned data to calculate the carbon emissions through the cleaned data.

[0069] In a specific embodiment, in the above step S101, raw data is collected in real time through deployed sensors and monitoring equipment. The raw data may include relevant data such as energy consumption, transportation activities, energy use and generation process, etc. By setting a data collection plan and frequency, timely and accurate data acquisition is ensured. Among them, data on energy use and production process can be obtained by establishing a data sharing mechanism with suppliers and partners.

[0070] In a specific embodiment, the original data can be classified into necessary data, desirable data, and possible data. Examples of common data in the steel industry are shown in the following table:

[0071]

[0072] In a specific embodiment, the preset data range in the above step S102 may be a common data range, where the so-called common data range refers to a common value range under normal circumstances, which may be determined by industry big data.

[0073] In a specific embodiment, in the above step S103, it is possible to determine which data ranges in the preset data range are the data ranges involved in the original data by matching the attribute information.

[0074] In a specific embodiment, in the above step S104, the original data can be screened by comparing the original data with a common value range in general situations that matches it, determining the data to be processed that meets the data range and the non-processed data that does not meet the data range, and determining whether the non-processed data is necessary data through its attribute information. If it is necessary data or desirable data, and the non-processed data is the only data with its corresponding attribute in the original data, manual processing is performed; if it is desirable data, deletion is performed.

[0075] In a specific embodiment, the validity judgment in the above step S105 may include a data type check, a data format check, a data valid range check, a data uniqueness check, a data consistency check, and a data integrity check.

[0076] in,

[0077] The data type check is used to determine whether the data to be processed conforms to the data type of the attribute corresponding to it;

[0078] Data format checking is used to determine whether the data to be processed meets the format requirements of the attributes corresponding to it;

[0079] The data valid range check is used to determine whether the data to be processed is within the data range set according to enterprise requirements;

[0080] Data uniqueness check is used to determine whether there are duplicate data in the data to be processed;

[0081] Data consistency check is used to determine whether the data to be processed is consistent between different data sets or fields;

[0082] Data integrity check is used to determine whether there are any missing data to be processed.

[0083] In a specific embodiment, the first classification result in the above step S106 may include type abnormal data, format abnormal data, effective range abnormal data, uniqueness abnormal data, consistency abnormal data and integrity abnormal data.

[0084] In a specific embodiment, in the above step S107, taking different measures to process the first classification result may include:

[0085] Standardize type-abnormal data and format-abnormal data based on preset standards; delete or manually process effective range-abnormal data; delete uniqueness-abnormal data; check and process consistency-abnormal data; perform deductive calculation or manual processing on integrity-abnormal data.

[0086] in,

[0087] Standardization can be performed through absolute value conversion, unit conversion, table lookup, data correction, and calculation based on parameter definition;

[0088] Determine the classification of abnormal data within the effective range. If it is required data, delete it; if it is necessary data or desirable data, manually process it;

[0089] Since data with the same attribute value is considered as duplicate data, the equality of the data to be processed is detected by judging whether the attribute values ​​between the data to be processed are equal. The equal data to be processed are deleted so that only one piece of data is retained;

[0090] Conduct retrospective investigation on consistency abnormal data, determine the abnormal cause, and perform manual processing;

[0091] Integrity abnormal data, that is, missing values, can be derived from the data source by using interpolation, average, maximum, minimum or more complex probability estimation methods, or filled with empirical values ​​through manual input.

[0092] In a specific embodiment, the rationality judgment in the above step S109 may include a data comprehensiveness check, a data reasonable range check, a data logic check, and a data real-time check.

[0093] in,

[0094] The data comprehensiveness check is used to check whether the first data contains all the preset attributes set according to the enterprise requirements;

[0095] The data reasonable range check is used to check whether the first data is within a reasonable range set according to the enterprise needs;

[0096] Data logic check is used to check whether the associated data conforms to the preset logic;

[0097] The data real-time check is used to check whether the first data is data collected within a preset time range.

[0098] In a specific embodiment, the second classification result in the above step S110 may include comprehensive abnormal data, reasonable range abnormal data, logical abnormal data and real-time abnormal data.

[0099] In a specific embodiment, in the above step S111, taking different measures to process the second classification result may include:

[0100] Investigate and process comprehensive abnormal data and real-time abnormal data; delete or manually process abnormal data within a reasonable range; and manually process logical abnormal data.

[0101] in,

[0102] Conduct retrospective investigation on comprehensive abnormal data and manually process the results after obtaining them;

[0103] Delete real-time abnormal data, investigate the causes of real-time abnormal data, and perform manual processing after finding the causes;

[0104] Determine the classification of abnormal data within a reasonable range. If it is required data, delete it; if it is necessary data or desirable data, handle it manually.

[0105] In a specific embodiment, a secondary validity and rationality judgment is performed in the above step S113, and the method is the same as the primary judgment, which will not be repeated here; after the judgment is completed, statistical analysis methods can be used to identify possible erroneous values ​​or abnormal values, such as deviation analysis, identifying values ​​that do not comply with distribution or regression equations, and simple rule libraries (common sense rules, business-specific rules, etc.) can be used to check data values, or constraints between different attributes and external data can be used to detect and clean data.

[0106] It can be seen from the above technical solutions of the embodiments of the present application that, in the embodiments of the present application, different processing methods are adopted for different abnormal data according to the characteristics of various types of abnormal data, so as to avoid the loss of important data due to a single processing method, thereby affecting the reliability of the cleaned data. In addition, the cleaned data is further judged for its validity and rationality to ensure that the cleaned data meets the requirements, and the reliability of the cleaned data can be improved, thereby achieving the effect of improving the integrity of the effective data and the accuracy of the carbon emission calculation.

[0107] Corresponding to the enterprise carbon emission data management method provided in the above embodiment, the embodiment of the present application also provides an enterprise carbon emission data management system. Since the enterprise carbon emission data management system provided in the embodiment of the present application corresponds to the enterprise carbon emission data management method provided in the above embodiment, the implementation method of the aforementioned enterprise carbon emission data management method is also applicable to the enterprise carbon emission data management system provided in this embodiment, and will not be described in detail in this embodiment.

[0108] See also Figure 2 , which is a structural block diagram of an enterprise carbon emission data management system provided by an embodiment of the present application; the system includes:

[0109] 001: Raw data collection module, used to collect raw data related to carbon emissions; the raw data carries original attribute information;

[0110] 002: a preset range acquisition module, used to acquire a preset data range; the preset data range carries preset attribute information;

[0111] 003: an attribute matching module, used to match the original attribute information with the preset attribute information and determine the data range corresponding to the original data;

[0112] 004: Data screening module, used to screen the original data based on the data range to obtain the data to be processed that meets the data range;

[0113] 005: a validity judgment module, used to judge the validity of the data to be processed, obtain the first intermediate data that meets the preset validity condition and the first abnormal data that does not meet the preset validity condition, and mark the first abnormal data so that the first abnormal data carries a first abnormal mark;

[0114] 006: a first classification module, used to classify the first abnormal data according to the abnormal type of the first abnormal data to obtain a first classification result;

[0115] 007: a first data processing module, used to take different measures to process the first classification result to obtain second intermediate data;

[0116] 008: a first data merging module, used to merge the first intermediate data and the second intermediate data to obtain first data;

[0117] 009: a rationality judgment module, configured to perform rationality judgment on the first data, obtain third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and perform a marking process on the second abnormal data so that the second abnormal data carries a second abnormal mark;

[0118] 010: a second classification module, used to classify the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result;

[0119] 011: a second data processing module, configured to take different measures to process the second classification result to obtain fourth intermediate data;

[0120] 012: a second data merging module, used for merging the third intermediate data and the fourth intermediate data to obtain second data;

[0121] 013: a second judgment module, used for successively performing validity judgment and rationality judgment on the second data;

[0122] 014: A cleaned data determination module, used to use the second data as cleaned data when the second data meets a preset validity condition and a preset rationality condition, so as to calculate carbon emissions through the cleaned data.

[0123] It should be noted that the system provided in the above embodiment, when implementing its functions, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0124] The enterprise carbon emission data management system of the embodiment of the present application adopts different processing methods for different abnormal data according to the characteristics of various abnormal data, so as to avoid the loss of important data due to a single processing method, thereby affecting the reliability of the cleaned data. In addition, the cleaned data is further judged for validity and rationality to ensure that the cleaned data meets the requirements and can improve the reliability of the cleaned data, thereby achieving the effect of improving the integrity of effective data and the accuracy of carbon emission calculations.

[0125] An embodiment of the present application also provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the enterprise carbon emission data management method provided in the above method embodiment.

[0126] The memory can be used to store software programs and modules. The processor executes various functional applications and realizes high-level autonomous driving by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, application programs required for functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0127] The method embodiments provided in the embodiments of the present application may be executed in a computer terminal, a server or a similar computing device, that is, the above-mentioned electronic device may include a computer terminal, a server or a similar computing device.

[0128] Figure 3 is a hardware structure block diagram of an electronic device for running an enterprise carbon emission data management method provided in an embodiment of the present application, such as Figure 3 As shown, the internal structure of the electronic device may include but is not limited to: a processor, a network interface and a memory. The processor, network interface and memory in the electronic device may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0129] Among them, the processor (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device. The network interface may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). Memory is a memory device in an electronic device for storing programs and data. It is understandable that the memory here can be a high-speed RAM storage device or a non-volatile memory device, such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor. The memory provides a storage space, which stores the operating system of the electronic device, which may include but is not limited to: Windows system (an operating system), Linux (an operating system), Android (Android, a mobile operating system) system, IOS (a mobile operating system) system, etc., and this application does not limit this; and, in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and these instructions may be one or more computer programs (including program codes). In the embodiment of this specification, the processor loads and executes one or more instructions stored in the memory to implement the enterprise carbon emission data management method provided in the above method embodiment.

[0130] An embodiment of the present application also provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the enterprise carbon emission data management method provided in the method embodiment.

[0131] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0132] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. And the above describes a specific embodiment of this specification. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-small sample image classification and parallel processing are also possible or may be advantageous.

[0133] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0134] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0135] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for managing enterprise carbon emission data, characterized in that: include: Collect raw data related to carbon emissions; The original data carries original attribute information; Acquire a preset data range; the preset data range carries preset attribute information; Matching the original attribute information with the preset attribute information to determine a data range corresponding to the original data; Screening the original data based on the data range to obtain data to be processed that meets the data range; Performing validity judgment on the data to be processed to obtain first intermediate data that meets a preset validity condition and first abnormal data that does not meet the preset validity condition, and marking the first abnormal data so that the first abnormal data carries a first abnormal mark; classifying the first abnormal data according to the abnormal type of the first abnormal data to obtain a first classification result; Taking different measures to process the first classification result to obtain second intermediate data; Merging the first intermediate data and the second intermediate data to obtain first data; Performing a rationality judgment on the first data to obtain third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and marking the second abnormal data so that the second abnormal data carries a second abnormal mark; classifying the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result; Taking different measures to process the second classification result to obtain fourth intermediate data; Combining the third intermediate data with the fourth intermediate data to obtain second data; Performing the validity judgment and the rationality judgment on the second data in sequence; In the case where the second data meets the preset validity condition and the preset rationality condition, the second data is used as cleaned data to perform carbon emission calculation based on the cleaned data.

2. The enterprise carbon emission data management method according to claim 1, characterized in that: The validity judgment includes data type check, data format check, data valid range check, data uniqueness check, data consistency check and data integrity check.

3. The enterprise carbon emission data management method according to claim 2, characterized in that: The first classification result includes type abnormal data, format abnormal data, effective range abnormal data, uniqueness abnormal data, consistency abnormal data and integrity abnormal data.

4. The enterprise carbon emission data management method according to claim 3 is characterized in that: The taking different measures to process the data of the first classification result includes: The type abnormal data and the format abnormal data are standardized based on preset standards; the effective range abnormal data are deleted or manually processed; the uniqueness abnormal data are deleted; the consistency abnormal data are checked; the integrity abnormal data are deduced or manually processed.

5. The enterprise carbon emission data management method according to claim 1, characterized in that: The rationality judgment includes data comprehensiveness check, data reasonable range check, data logic check and data real-time check.

6. The enterprise carbon emission data management method according to claim 5, characterized in that: The second classification results include comprehensive abnormal data, reasonable range abnormal data, logical abnormal data and real-time abnormal data.

7. The enterprise carbon emission data management method according to claim 6, characterized in that: The taking different measures to process the data of the second classification result includes: The comprehensive abnormal data and the real-time abnormal data are checked and processed; the abnormal data within the reasonable range is deleted or manually processed; and the logical abnormal data is manually processed.

8. An enterprise carbon emission data management system, characterized in that: The system comprises: A raw data collection module, used to collect raw data related to carbon emissions; the raw data carries raw attribute information; A preset range acquisition module, used to acquire a preset data range; the preset data range carries preset attribute information; An attribute matching module, used to match the original attribute information with the preset attribute information to determine a data range corresponding to the original data; A data screening module, used for screening the original data based on the data range to obtain the data to be processed that meets the data range; A validity judgment module, used to judge the validity of the data to be processed, obtain first intermediate data that meets the preset validity condition and first abnormal data that does not meet the preset validity condition, and mark the first abnormal data so that the first abnormal data carries a first abnormal mark; A first classification module, configured to classify the first abnormal data according to an abnormal type of the first abnormal data to obtain a first classification result; A first data processing module, used for taking different measures to process the first classification result to obtain second intermediate data; A first data merging module, used for merging the first intermediate data and the second intermediate data to obtain first data; a rationality judgment module, configured to perform a rationality judgment on the first data, obtain third intermediate data that meets a preset rationality condition and second abnormal data that does not meet the preset rationality condition, and perform a marking process on the second abnormal data so that the second abnormal data carries a second abnormal mark; A second classification module, used for classifying the second abnormal data according to the abnormal type of the second abnormal data to obtain a second classification result; A second data processing module, used for taking different measures to process the second classification result to obtain fourth intermediate data; A second data merging module, used for merging the third intermediate data and the fourth intermediate data to obtain second data; a second judgment module, used for sequentially performing the validity judgment and the rationality judgment on the second data; The cleaned data determination module is used to use the second data as cleaned data when the second data meets the preset validity condition and the preset rationality condition, so as to calculate carbon emissions through the cleaned data.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the enterprise carbon emission data management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the enterprise carbon emission data management method as described in any one of claims 1 to 7.

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