A carbon emission data processing method and system

Abstract

This invention discloses a carbon emission data processing method and system. The method includes: acquiring carbon emission data from carbon emission sources within a preset time period; clustering the data according to the rate of change in carbon emissions to obtain multiple data sets; selecting target data that meets integrity conditions from each set; performing a first desensitization process using a deep learning-based data recognition model and dynamic bounding box technology to obtain sensitive and non-sensitive sub-data; based on the target sensitive sub-data, performing a second desensitization process on other data within the set through bounding box translation or template matching; finally, encrypting and storing the sensitive sub-data in a first database, and directly storing the non-sensitive sub-data in a second database. This invention achieves intelligent classification and accurate desensitization of carbon emission data, significantly improving processing efficiency while ensuring data security, and solving the problems of insufficient desensitization accuracy, low processing efficiency, and high resource consumption in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a carbon emission data processing method and system. Background Technology

[0002] The monitoring, reporting, and verification of carbon emission data have become an important part of corporate environmental responsibility and compliance management. Various industrial enterprises, energy facilities, and other carbon emission sources generate massive amounts of carbon emission data. This data not only includes basic emission figures but also sensitive information such as enterprise production processes, energy consumption, and technological efficiency, possessing extremely high commercial value and requiring confidentiality. Therefore, how to effectively collect, process, store, and analyze carbon emission data while ensuring data security has become a key technological challenge.

[0003] Currently, in the field of carbon emission data processing, especially in terms of data security and desensitization, the following technical solutions and their limitations mainly exist:

[0004] First, traditional static data anonymization techniques typically rely on predefined rule bases or simple regular expression matching. For example, they identify specific field names (such as "Company ID," "emissions," etc.) and mask, replace, or hash the corresponding field values. While this method is simple to implement, it has significant shortcomings when dealing with complex and variable carbon emission data: First, the rule base requires manual maintenance and updates, making it difficult to adapt to dynamic changes in data patterns and resulting in limited coverage; second, it lacks a deep understanding of data semantics, easily leading to misjudgments (incorrectly anonymizing non-sensitive information) or omissions (failing to identify variant forms of sensitive information), severely impacting the accuracy of anonymization and the subsequent usability of the data.

[0005] Secondly, existing data encryption and storage solutions often adopt a "one-size-fits-all" strategy, that is, uniformly encrypting the entire dataset. While this ensures data security to a certain extent, it leads to encryption operations consuming a large amount of computing resources, especially when processing large-scale carbon emission time series data, where system throughput and response time are severely affected. Summary of the Invention

[0006] This invention provides a carbon emission data processing method and system to solve the technical problem that causes encryption operations to consume a large amount of computing resources.

[0007] In a first aspect, the present invention provides a carbon emission data processing method, comprising:

[0008] Acquire carbon emission data from carbon emission sources within a preset time period, and cluster at least one carbon emission data set with the same rate of change in carbon emission to obtain at least one carbon emission data set.

[0009] Extract a target carbon emission data from a certain carbon emission dataset, and perform a first desensitization process on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data.

[0010] Based on the target sensitive carbon emission sub-data, a preset second data desensitization strategy is used to perform a second desensitization process on other carbon emission data in the target carbon emission dataset to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data.

[0011] According to preset data encryption rules, the sensitive carbon emission sub-data of a certain target and the other sensitive carbon emission sub-data are encrypted and stored in a preset first database.

[0012] The non-sensitive carbon emission sub-data of a certain target and the other non-sensitive carbon emission sub-data are directly stored in a preset second database.

[0013] In a second aspect, the present invention provides a carbon emission data processing system, comprising:

[0014] The acquisition module is configured to acquire carbon emission data of carbon emission sources within a preset time period, and cluster at least one carbon emission data with the same rate of change of carbon emission to obtain at least one carbon emission data set.

[0015] The first processing module is configured to extract a target carbon emission data from a certain carbon emission data set, and perform a first desensitization process on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data.

[0016] The second processing module is configured to perform a second desensitization process on other carbon emission data in the carbon emission data set based on the target sensitive carbon emission sub-data and a preset second data desensitization strategy, so as to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data.

[0017] The first storage module is configured to encrypt and store the target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data respectively into a preset first database according to a preset data encryption rule;

[0018] The second storage module is configured to directly store the target non-sensitive carbon emission sub-data and the other non-sensitive carbon emission sub-data into a preset second database.

[0019] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the carbon emission data processing method of any embodiment of the present invention.

[0020] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the carbon emission data processing method of any embodiment of the present invention.

[0021] The carbon emission data processing method and system of this application acquires carbon emission data from carbon emission sources within a preset time period, clusters the data according to the rate of change of carbon emissions to obtain multiple data sets, selects target data that meets the integrity condition in each set, and performs a first desensitization process using a data recognition model based on deep learning and dynamic bounding box technology to obtain sensitive and non-sensitive sub-data. Based on the target sensitive sub-data, a second desensitization process is performed on other data in the set through bounding box translation or template matching. Finally, the sensitive sub-data is encrypted and stored in a first database, while the non-sensitive sub-data is directly stored in a second database. This achieves intelligent classification and accurate desensitization of carbon emission data, significantly improves processing efficiency while ensuring data security, and solves the problems of insufficient desensitization accuracy, low processing efficiency, and high resource consumption in existing technologies. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a carbon emission data processing method according to an embodiment of the present invention;

[0024] Figure 2 This is a structural block diagram of a carbon emission data processing system provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please refer to Figure 1 The diagram shows a flowchart of a carbon emission data processing method according to this application.

[0028] like Figure 1 As shown, the carbon emission data processing method specifically includes the following steps:

[0029] Step S101: Obtain carbon emission data of carbon emission sources within a preset time period, and cluster at least one carbon emission data with the same rate of change in carbon emission to obtain at least one carbon emission data set.

[0030] In this step, after obtaining carbon emission data from carbon emission sources within a preset time period, the rate of change of carbon emissions between the initial carbon emission data at the initial time node and other carbon emission data at other time nodes is calculated. The initial time node is the first time node of the preset time period, and other time nodes are any time nodes in the preset time period excluding the initial time node. The difference in the rate of change between each carbon emission rate of change is calculated, and at least one carbon emission data with a difference in the rate of change within a preset range is assigned to the same carbon emission data set to obtain at least one carbon emission data set.

[0031] In one specific embodiment, a sequence of carbon emission data recorded at fixed time intervals (e.g., daily, monthly) for a specified carbon emission source (such as a boiler or a production line) within a preset time period (e.g., the past year) is obtained.

[0032] The carbon emission data D1 at the initial time point was used as the calculation benchmark.

[0033] Calculate the rate of change Ri of carbon emission data Di relative to carbon emission data D1 for each other time point i (i from 2 to n). The rate of change can be calculated using a relative rate of change formula, for example: Ri = (Di - D1) / D1, which measures the proportion of increase or decrease relative to the initial value;

[0034] This calculation yields a sequence of rates of change: [R1, R2, R3, ..., Rn].

[0035] Calculate the absolute difference between any two rates of change in the above rate of change sequence. Assuming that the differences between any two of R1, R2, and R3 are less than the preset range ε, then their corresponding data D1, D2, and D3 are classified into the same carbon emission data set.

[0036] In this embodiment, the rate of change under a unified standard is calculated by comparing the data at each time point with a common initial baseline. Then, clustering is performed based on the similarity between the rates of change (controlled by a preset threshold). This method can effectively group carbon emission data with similar dynamic change characteristics, facilitating efficient and consistent data anonymization and security processing for data sets with different "change patterns" in the future.

[0037] Step S102: Extract a target carbon emission data from a certain carbon emission data set, and perform a first desensitization process on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data.

[0038] In this step, carbon emission data in a given carbon emission dataset are sorted according to their chronological order to obtain a carbon emission data sequence corresponding to that dataset. The earliest carbon emission data in this sequence is selected as candidate carbon emission data, and it is determined whether the candidate carbon emission data meets preset integrity conditions. These integrity conditions include: the data format of the candidate carbon emission data conforms to preset specifications, and the rate of change between the candidate carbon emission data and adjacent carbon emission data meets preset continuity requirements. If the candidate carbon emission data meets the integrity conditions, it is identified as a target carbon emission data. If the candidate carbon emission data does not meet the integrity conditions, the next carbon emission data in the sequence is selected as a new candidate carbon emission data in chronological order, and the integrity judgment is repeated until a target carbon emission data that meets the integrity conditions is determined.

[0039] It should be noted that the data format of the candidate carbon emission data conforms to the preset specifications, specifically:

[0040] Structural integrity: Check that the data contains all required fields (e.g., timestamp, emission values, emission source ID, fuel type, activity data, etc.). Missing critical fields are not allowed.

[0041] Data type and format: Check whether the value of each field conforms to the defined format. For example: whether the timestamp is a valid date and time format; whether the emission amount is a valid numeric type (such as an integer or floating-point number) and is non-negative; and whether text fields (such as emission source ID) conform to the predefined encoding rules.

[0042] Value range reasonableness: Check whether the value is within a reasonable physical range. For example, whether there are extremely abnormal values ​​in the emissions (such as values ​​that far exceed historical records or equipment capacity).

[0043] If any format check fails, the condition is deemed not met.

[0044] The requirement that the rate of change between the candidate carbon emission data and its adjacent carbon emission data meets the preset continuity requirement specifically means: calculating the instantaneous rate of change between the candidate carbon emission data and its previous and next adjacent data (if any), and judging its rationality.

[0045] Calculate the rate of change: Based on (current data - adjacent data) / adjacent data, calculate the rate of change compared to the previous data. The rate of change of the sum with the next data This rate of change measures the fluctuation between adjacent time points.

[0046] Set a continuity threshold: The system presets a continuity threshold. (For example =0.15 (i.e., 15%), this threshold is set based on historical data fluctuation patterns or domain knowledge, and is used to identify mutations or anomalies.

[0047] If the candidate data is the first data in the sequence, then only check its rate of change relative to the next data. Is the absolute value ≤ ;

[0048] If the candidate data is the last data in the sequence, only check its rate of change compared to the previous data. Is the absolute value ≤ ;

[0049] If the candidate data is in the middle of the sequence, then it needs to simultaneously satisfy the following conditions: ≤ and ≤ .

[0050] This check aims to filter out "glitch" data caused by data logging errors, temporary equipment malfunctions, or special events. If the rate of change exceeds a threshold, the condition is deemed not met.

[0051] Furthermore, according to a preset first data desensitization strategy, the carbon emission data of a certain target is subjected to a first desensitization process to obtain sensitive carbon emission sub-data and non-sensitive carbon emission sub-data of a certain target, including:

[0052] Input the carbon emission data of a certain target into a preset data recognition model, and the data recognition model outputs the location information of at least one sensitive field in the carbon emission data of the certain target.

[0053] Specifically, the data recognition model is built on a deep learning architecture (such as BERT, CNN-BiLSTM-CRF) and is specifically trained for carbon emission data formats. When a target carbon emission data is input into the data recognition model, the data recognition model outputs the location information of at least one sensitive field in the target carbon emission data, wherein the target carbon emission data is in the format of structured data or standardized text.

[0054] Based on the location information of at least one sensitive field, carbon emission data of a certain target is extracted using a preset data extraction rule to obtain sensitive carbon emission sub-data and non-sensitive carbon emission sub-data of a certain target.

[0055] Specifically, two adjacent sensitive fields with a position interval less than a preset threshold are grouped into the same sensitive field sequence to obtain at least one sensitive field sequence; a bounding box is constructed with a target sensitive field in a sensitive field sequence as the center point and a dynamic length as the boundary length; based on the bounding box, data content of a sensitive field containing the sensitive field sequence is extracted from the target carbon emission data, where the dynamic length is the sum of the sequence length and the fixed length of the sensitive field sequence, and the target sensitive field is the sensitive field located at the center of the sensitive field sequence; all extracted sensitive field data content is combined to obtain a target sensitive carbon emission sub-data; all sensitive field data content is deleted from the target carbon emission data, and the remaining data content is combined to obtain a target non-sensitive carbon emission sub-data.

[0056] Step S103: Based on the target sensitive carbon emission sub-data, a preset second data desensitization strategy is used to perform a second desensitization process on other carbon emission data in the target carbon emission data set to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data.

[0057] In this step, the first data length of the first carbon emission data is obtained, and it is determined whether the first data length is consistent with the target data length of a certain target carbon emission data.

[0058] If they match, the first carbon emission data is directly aligned with a target carbon emission data, and the bounding boxes set on the target carbon emission data are shifted to the same positions in the first carbon emission data to extract the data of each first sensitive field.

[0059] All extracted first sensitive field data contents are combined to obtain first sensitive carbon emission sub-data. Then, all first sensitive field data contents are deleted from the first carbon emission data, and the remaining data contents are combined to obtain first non-sensitive carbon emission sub-data.

[0060] If there is a discrepancy, the structured features of a certain target sensitive carbon emission sub-data are extracted to generate a standard sensitive data template. The standard sensitive data template contains the type of the standard sensitive field, the position of the standard sensitive field, and the length of the bounding box corresponding to the standard sensitive field.

[0061] Specifically, structured features refer to the type of each standard sensitive field, the position of each standard sensitive field, and the length of the bounding box corresponding to each standard sensitive field. Therefore, a pre-trained semantic recognition model can identify the semantics of a specific target sensitive carbon emission subdata, thereby obtaining the type of each standard sensitive field, and determining the position of each standard sensitive field and the length of the bounding box corresponding to each standard sensitive field based on step S102. Here, a bounding box corresponding to a certain standard sensitive field is the bounding box on the sensitive field sequence containing that standard sensitive field, and this bounding box contains at least one standard sensitive field.

[0062] Sensitive fields identified in the first carbon emission data are extracted according to the standard sensitive data template to generate first sensitive carbon emission sub-data and first non-sensitive carbon emission sub-data.

[0063] Specifically, the first carbon emission data is input into the data identification model to obtain the corresponding sensitive fields. Based on the type of the standard sensitive fields, the same type of standard sensitive fields are searched in the standard sensitive data template. According to the location of the standard sensitive fields and the length of the bounding box corresponding to the standard sensitive fields, the corresponding first sensitive field data content is extracted from the first carbon emission data. Here, the first sensitive field data content refers to the sensitive field data content in the first carbon emission data.

[0064] In this embodiment, it is determined whether the length of the first data is consistent with the length of the target data of a certain target carbon emission data. If they are consistent, the first carbon emission data is directly aligned with the target carbon emission data, and the bounding boxes set on the target carbon emission data are translated to the same position of the first carbon emission data to extract the data of each first sensitive field. If they are inconsistent, a strategy of first selecting high-quality target data to establish a desensitization template and then extending it to other data in the same set is adopted. This achieves the effect of batch processing with a "point-to-surface" approach, avoiding the resource consumption of complex rule configuration or model reasoning for each piece of data in the prior art. While ensuring the consistency of desensitization, it greatly reduces the computational overhead and time cost, providing a practical and feasible technical path for the efficient and secure processing of large-scale carbon emission data.

[0065] Step S104: According to the preset data encryption rules, the sensitive carbon emission sub-data of a certain target and the other sensitive carbon emission sub-data are encrypted and stored in the preset first database.

[0066] In this step, data standardization processing is performed on a specific target sensitive carbon emission sub-data and other sensitive carbon emission sub-data to obtain standardized sensitive carbon emission sub-data. The data standardization processing includes: uniformly converting the data format of the sensitive carbon emission sub-data and removing redundant information; encrypting the standardized sensitive carbon emission sub-data using a preset encryption algorithm to obtain encrypted sensitive carbon emission sub-data and storing it in a preset first database. The encryption algorithm includes a symmetric encryption algorithm or an asymmetric encryption algorithm.

[0067] It should be noted that both symmetric and asymmetric encryption algorithms are conventional encryption algorithms, so they will not be discussed in detail here.

[0068] Step S105: Directly store the target non-sensitive carbon emission sub-data and the other non-sensitive carbon emission sub-data into a preset second database.

[0069] In summary, the method of this application, in terms of data processing efficiency, automatically groups data with similar dynamic characteristics through a clustering mechanism based on the rate of change of carbon emissions (step S101), creating favorable conditions for subsequent batch processing. Combined with the strategy of selecting representative target data within the cluster set to establish a desensitization template and then applying it (steps S102-S103), a highly efficient processing paradigm of "classification-sample processing-batch expansion" is formed. This effectively avoids the repetitive computational overhead caused by complex rule configuration or model inference for each data point in existing technologies, resulting in an order-of-magnitude improvement in the processing efficiency of large-scale carbon emission data. Secondly, in terms of data security and accurate desensitization, a data recognition model based on deep learning and dynamic bounding box extraction technology are adopted (step S102), achieving fine-grained and adaptive extraction of sensitive information, reducing the shortcomings of incomplete coverage and high false positive rates inherent in traditional fixed-rule desensitization methods. Furthermore, regarding system adaptability and scalability, a dual processing path (step S103) was designed to handle inconsistent data lengths. This supports rapid bounding box translation for data of the same length and adapts to data of different lengths using a standard sensitive data template, demonstrating excellent system robustness. Finally, in terms of data storage and management optimization, a separate architecture (steps S104-S105) was adopted to ensure the security of core data while maximizing the availability and query efficiency of non-sensitive data, facilitating subsequent data analysis and sharing. In summary, significant improvements were achieved in data processing efficiency, security, adaptability, and resource utilization, providing a comprehensive technical solution for the large-scale, secure, and efficient processing of carbon emission data.

[0070] Please see Figure 2 The diagram shows a structural block diagram of a carbon emission data processing system according to this application.

[0071] like Figure 2 As shown, the carbon emission data processing system 200 includes an acquisition module 210, a first processing module 220, a second processing module 230, a first storage module 240, and a second storage module 250.

[0072] The acquisition module 210 is configured to acquire carbon emission data of carbon emission sources within a preset time period, and cluster at least one carbon emission data with the same rate of change in carbon emission to obtain at least one carbon emission data set; the first processing module 220 is configured to extract a target carbon emission data from a carbon emission data set, and perform a first desensitization processing on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data; the second processing module 230 is configured to, based on the target sensitive carbon emission sub-data... Carbon emission sub-data is processed by using a preset second data desensitization strategy to perform a second desensitization process on other carbon emission data in a certain carbon emission dataset, resulting in other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data; a first storage module 240 is configured to encrypt and store the target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data respectively in a preset first database according to a preset data encryption rule; a second storage module 250 is configured to directly store the target non-sensitive carbon emission sub-data and the other non-sensitive carbon emission sub-data in a preset second database.

[0073] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0074] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the carbon emission data processing method in any of the above method embodiments;

[0075] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0076] Acquire carbon emission data from carbon emission sources within a preset time period, and cluster at least one carbon emission data set with the same rate of change in carbon emission to obtain at least one carbon emission data set.

[0077] Extract a target carbon emission data from a certain carbon emission dataset, and perform a first desensitization process on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data.

[0078] Based on the target sensitive carbon emission sub-data, a preset second data desensitization strategy is used to perform a second desensitization process on other carbon emission data in the target carbon emission dataset to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data.

[0079] According to preset data encryption rules, the sensitive carbon emission sub-data of a certain target and the other sensitive carbon emission sub-data are encrypted and stored in a preset first database.

[0080] The non-sensitive carbon emission sub-data of a certain target and the other non-sensitive carbon emission sub-data are directly stored in a preset second database.

[0081] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the carbon emission data processing system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the carbon emission data processing system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0082] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the carbon emission data processing method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the carbon emission data processing system. The output device 340 may include a display screen or other display device.

[0083] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0084] In one implementation, the above-described electronic device is used in a carbon emission data processing system as a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0085] Acquire carbon emission data from carbon emission sources within a preset time period, and cluster at least one carbon emission data set with the same rate of change in carbon emission to obtain at least one carbon emission data set.

[0086] Extract a target carbon emission data from a certain carbon emission dataset, and perform a first desensitization process on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data.

[0087] Based on the target sensitive carbon emission sub-data, a preset second data desensitization strategy is used to perform a second desensitization process on other carbon emission data in the target carbon emission dataset to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data.

[0088] According to preset data encryption rules, the sensitive carbon emission sub-data of a certain target and the other sensitive carbon emission sub-data are encrypted and stored in a preset first database.

[0089] The non-sensitive carbon emission sub-data of a certain target and the other non-sensitive carbon emission sub-data are directly stored in a preset second database.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon emission data processing method, characterized by, The method comprises the following steps: obtaining carbon emission data of a carbon emission source in a preset time period, and clustering at least one carbon emission data with the same carbon emission change rate to obtain at least one carbon emission data set; extracting a target carbon emission data in a certain carbon emission data set, and performing first desensitization processing on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data, comprising: inputting the target carbon emission data into a preset data recognition model, and obtaining position information of at least one sensitive field in the target carbon emission data through the data recognition model; according to the position information of the at least one sensitive field, using a preset data interception rule to intercept the target carbon emission data to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data, comprising: dividing adjacent two sensitive fields smaller than a preset position interval threshold into the same sensitive field sequence to obtain at least one sensitive field sequence; taking a target sensitive field in a certain sensitive field sequence as a center point and a certain dynamic length as a boundary length to construct a certain boundary box, and extracting a certain sensitive field data content containing the certain sensitive field sequence based on the certain boundary box on the target carbon emission data, wherein the certain dynamic length is the sum of the sequence length of the certain sensitive field sequence and the fixed length, and the target sensitive field is the sensitive field at the center position in the certain sensitive field sequence; combining all the extracted sensitive field data contents to obtain the target sensitive carbon emission sub-data; deleting all the sensitive field data contents from the target carbon emission data and combining the remaining data contents to obtain a target non-sensitive carbon emission sub-data; according to the target sensitive carbon emission sub-data, using a preset second data desensitization strategy to perform second desensitization processing on other carbon emission data in the certain carbon emission data set to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data, comprising: obtaining a first data length of a first carbon emission data, and determining whether the first data length is consistent with a target data length of the target carbon emission data; if consistent, directly aligning the first carbon emission data with the target carbon emission data, and translating each boundary box set on the target carbon emission data to the same position of the first carbon emission data to extract each first sensitive field data content; combining all the extracted first sensitive field data contents to obtain a first sensitive carbon emission sub-data, and deleting all the first sensitive field data contents from the first carbon emission data to combine the remaining data contents to obtain a first non-sensitive carbon emission sub-data; if inconsistent, extracting the structured features of the target sensitive carbon emission sub-data to generate a standard sensitive data template; extracting the sensitive fields identified in the first carbon emission data according to the standard sensitive data template to generate first sensitive carbon emission sub-data and first non-sensitive carbon emission sub-data; storing the certain target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data in the first database according to a preset data encryption rule; storing the certain target non-sensitive carbon emission sub-data and the other non-sensitive carbon emission sub-data in the second database.

2. The carbon emission data processing method of claim 1, wherein, The clustering of at least one carbon emission data with the same carbon emission change rate to obtain at least one carbon emission data set comprises: calculating the carbon emission change rate between the initial carbon emission data of the initial time node and the other carbon emission data of the other time node, wherein the initial time node is the first time node of the preset time period, and the other time node is any time node after the initial time node in the preset time period; calculating the change rate difference between each carbon emission change rate, and dividing at least one carbon emission data with a change rate difference within a preset range into the same carbon emission data set to obtain at least one carbon emission data set.

3. The carbon emission data processing method of claim 1, wherein, The extraction of a certain target carbon emission data in a certain carbon emission data set comprises: sorting each carbon emission data in the certain carbon emission data set according to the order of the time nodes to obtain a certain carbon emission data sequence corresponding to the certain carbon emission data set; selecting the carbon emission data with the earliest time node in the certain carbon emission data sequence as a candidate carbon emission data, and determining whether the candidate carbon emission data meets a preset integrity condition, wherein the integrity condition comprises that the data format of the candidate carbon emission data conforms to a preset specification and the change rate between the candidate carbon emission data and the adjacent carbon emission data meets a preset continuity requirement; if the candidate carbon emission data meets the integrity condition, the candidate carbon emission data is determined as the certain target carbon emission data; if the candidate carbon emission data does not meet the integrity condition, the next carbon emission data in the certain carbon emission data sequence is selected as a new candidate carbon emission data in time sequence, and the integrity condition is re-determined until the certain target carbon emission data meeting the integrity condition is determined.

4. The carbon emission data processing method of claim 1, wherein, The standard sensitive data template comprises the type of the standard sensitive field, the position of the standard sensitive field, and the length of the bounding box corresponding to the standard sensitive field.

5. The carbon emission data processing method of claim 1, wherein, The storing of the certain target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data in the first database according to a preset data encryption rule comprises: performing data standardization processing on the certain target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data to obtain standardized sensitive carbon emission sub-data, wherein the data standardization processing comprises uniformly converting the data format of the sensitive carbon emission sub-data and removing redundant information; The standardized sensitive carbon emission sub-data is processed by using a preset encryption algorithm to obtain encrypted sensitive carbon emission sub-data and stored in a preset first database, wherein the encryption algorithm includes a symmetric encryption algorithm or an asymmetric encryption algorithm.

6. A carbon emissions data processing system characterized by, Comprise: The acquisition module is configured to acquire carbon emission data of a carbon emission source within a preset time period, and cluster at least one carbon emission data with the same carbon emission rate to obtain at least one carbon emission data set; The first processing module is configured to extract a target carbon emission data in a certain carbon emission data set, and perform first desensitization processing on the target carbon emission data according to a preset first data desensitization strategy to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data associated with the target sensitive carbon emission sub-data, comprising: inputting the target carbon emission data into a preset data recognition model, and the data recognition model outputs the position information of at least one sensitive field in the target carbon emission data; According to the position information of the at least one sensitive field, a preset data interception rule is used to intercept the target carbon emission data to obtain a target sensitive carbon emission sub-data and a target non-sensitive carbon emission sub-data, comprising: Divide the adjacent two sensitive fields less than the preset position interval threshold into the same sensitive field sequence to obtain at least one sensitive field sequence; A certain boundary box is constructed with a certain target sensitive field in a certain sensitive field sequence as the center point and a certain dynamic length as the boundary length, and a certain sensitive field data content containing the certain sensitive field sequence is extracted based on the certain boundary box on the target carbon emission data, wherein the certain dynamic length is the sum of the sequence length and the fixed length of the certain sensitive field sequence, and the certain target sensitive field is the sensitive field in the certain sensitive field sequence at the center position; Combine all the sensitive field data contents extracted to obtain the target sensitive carbon emission sub-data; Delete all the sensitive field data contents from the target carbon emission data, and combine the remaining data contents to obtain a target non-sensitive carbon emission sub-data; The second processing module is configured to perform second desensitization processing on other carbon emission data in the certain carbon emission data set according to the target sensitive carbon emission sub-data by using a preset second data desensitization strategy to obtain other sensitive carbon emission sub-data and other non-sensitive carbon emission sub-data associated with the other sensitive carbon emission sub-data, comprising: Obtain the first data length of the first carbon emission data, and determine whether the first data length is consistent with the target data length of the target carbon emission data; If consistent, directly align the first carbon emission data with the target carbon emission data, and translate each boundary box set on the target carbon emission data to the same position of the first carbon emission data to extract each first sensitive field data content; combining all the first sensitive field data contents extracted to obtain first sensitive carbon emission sub-data, and deleting all the first sensitive field data contents from the first carbon emission data, and combining the remaining data contents to obtain first non-sensitive carbon emission sub-data; if not, extracting structured features of a certain target sensitive carbon emission sub-data to generate a standard sensitive data template; extracting the sensitive fields identified in the first carbon emission data according to the standard sensitive data template to generate first sensitive carbon emission sub-data and first non-sensitive carbon emission sub-data; a first storage module configured to respectively encrypt and store the certain target sensitive carbon emission sub-data and the other sensitive carbon emission sub-data into a preset first database according to a preset data encryption rule; a second storage module configured to directly store the certain target non-sensitive carbon emission sub-data and the other non-sensitive carbon emission sub-data into a preset second database.

7. An electronic device, comprising: comprise: at least one processor, and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1 to 5.

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